{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>气缸</th>\n",
       "      <th>排量</th>\n",
       "      <th>马力</th>\n",
       "      <th>重量</th>\n",
       "      <th>加速度</th>\n",
       "      <th>年份</th>\n",
       "      <th>产地</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  气缸     排量     马力      重量   加速度  年份  产地\n",
       "0  18.0   8  307.0  130.0  3504.0  12.0  70   1\n",
       "1  15.0   8  350.0  165.0  3693.0  11.5  70   1\n",
       "2  18.0   8  318.0  150.0  3436.0  11.0  70   1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# 下载数据集到本地\n",
    "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data\"\n",
    "dataset_path = keras.utils.get_file(\"auto-mpg.data\", url)\n",
    "\n",
    "# 使用Pandas读取数据\n",
    "column_names = ['MPG','气缸','排量','马力','重量','加速度', '年份', '产地']\n",
    "raw_dataset = pd.read_csv(dataset_path, names=column_names,\n",
    "                      na_values = \"?\", comment='\\t',\n",
    "                      sep=\" \", skipinitialspace=True)\n",
    "\n",
    "dataset = raw_dataset.copy()\n",
    "# 查看前3条数据\n",
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MPG    0\n",
       "气缸     0\n",
       "排量     0\n",
       "马力     6\n",
       "重量     0\n",
       "加速度    0\n",
       "年份     0\n",
       "产地     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MPG</th>\n",
       "      <th>气缸</th>\n",
       "      <th>排量</th>\n",
       "      <th>马力</th>\n",
       "      <th>重量</th>\n",
       "      <th>加速度</th>\n",
       "      <th>年份</th>\n",
       "      <th>美国</th>\n",
       "      <th>欧洲</th>\n",
       "      <th>日本</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>3693.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>3436.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MPG  气缸     排量     马力      重量   加速度  年份   美国   欧洲   日本\n",
       "0  18.0   8  307.0  130.0  3504.0  12.0  70  1.0  0.0  0.0\n",
       "1  15.0   8  350.0  165.0  3693.0  11.5  70  1.0  0.0  0.0\n",
       "2  18.0   8  318.0  150.0  3436.0  11.0  70  1.0  0.0  0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin = dataset.pop('产地')\n",
    "dataset['美国'] = (origin == 1)*1.0\n",
    "dataset['欧洲'] = (origin == 2)*1.0\n",
    "dataset['日本'] = (origin == 3)*1.0\n",
    "# 看一看转换后的结果\n",
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练集 80%， 测试集 20%\n",
    "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
    "test_dataset = dataset.drop(train_dataset.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1336cf4e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/+8Oc0cCF1xKLBuODBMgtwOPTOH66niv7ZMR7KQBc2TuD5ZsPcuBkLb0yk+K9HKGdU+/VjOAY/Ed6c97YytuzRjbr+pZ0XQdq/N6bM0a6ti8CwvkgP7q6mLfuHcmDb23jd3dewbLP9jNvQp5xmvHsX3bxq1vzyYrxCZ5oUYiEo95LYaM+jbmrinny+98yTtoOVzrxeDUgci3xe3PGRNRxYB/tlGDmncJRmBRQVVU02ApIgNwCjlfVo+kY03PizYjenVm++SB//voYc8ZKFlloGZHqQbVm1CBHo+taVZWYBz9C2yCSD7LL66O8xoVJVZhzbV8qa/2TyVLtFuZNyMPp9lJeTcyDBdGiEMDr1SirceHx+YPecLpNsJhItVtYOWskdW4fFpOK16vh9vp45rbBRinGtlLHWWuJxb0ivkiTXgsorfR7ILeVjTMz2Ubf7GQ+LD4W76UIHQCbWTXqPwP4697OvW1I17XQXDRNR0EJq7WTNW4WTs7HYlZxun3GKOqH393O4UonP377K25d9Bm7TlQ364ObILQEr1dj54lqbl+6iWsWbuBYVX1Y3Wal2Fj48U5jbPoRh5MDp2q546XN3PHSZuavKeHhGwYwJCftrLXEso/GFwmQW0BAtG0lQIYzbhaHGgaYCMKFogDP3j7Y+APQI93Os7cPpjl5C+m6FppLRa2b+Wu+4ZnbQrW2eFoBuq7z1Ee7qHOFH0U9e2yuBA1Cq1FW42J2Q0nFkJw0Eixqkz1yybQC3tx8gHUlZYBfqz96axulp5xNSogeHNfvrLXEso/GFymxaAGHK50o0KYK5a/s3dlws7hvrAwNES6ceq/Gr/+8M6Tu89d/3slv77zinNdK17XQXNxeH+tKyiivdjNvQh4Du6aw83g1//PHr9lW6gDAq2lnHe0rQYPQGnh9Z3Q4e2wuD7y5jaxkG09+/1t0TU3AZjbhqHOz9B8HQq47XOkk0Wpq8rvc7GR6pNkjlkvIPhpfJIPcAg5X1tE5yYq5FWyvmktWSgJ9s5JYUyxDQ4SWYVYVslJCP/xlpVgxN6P2LdB1HZxZka5rIRyWhlKebaUOCpcXoWk689eUGMExwMkaN+Pzslk6vYCVs0aydHoB4/OycTj9NckSNAixRtN0dDD2tDS7hcOVTraVOpj2yhdc/5tPKTl2mpM17rBlF3XuMx/ghuSksWzGcMDfgBepPEj20fgiGeQWcLjS2SY8kBtzZYMn8sGKWnpmiJuFcGGkJKj8aFx/Y5pe4Ng7JeHcHwil819oLmZVYeHkfKOEIlB3HGzplpZo5kfX9eO+RpaDKzYdlKBBaBUqat386sMSFkzK59HVxTicnibZ3dVFpTxy48Am+v3dnVeQarf465OTbc22LJR9NL5IgNwCjlTW0Ssz/h7IjRnZECD/aftRHriuX7yXI7RTalx62FHT7xSOopP9HBcjnf9C83C6fTz10S6jlMeraSTbzMyfOIhuqTaSbBYUBe58bXMTy8GVs0Yy1zxQggYh5jQuBbokNYEXpwzl/jfPfGj7yXcG0DM9kU4JFt6eNRKfpqMocKLKhc2s8oc5o/F4Ne54KVTLZ7N6k300fkiAfIH4NJ0Tp10M69U53ktpQmayjYFdU/jjV/6hIYoifziE8ye43i6Af9S0FqcVCR0Rq9lEeY2LwuVFxu/G52Xzi4mDOFnt4gcvb+aZ2wZHHOcrwYPQGgTqgQOlQODX6TuFo9B1HavZRLrdwp7ymhBbtgWT8nn6412U17h4b84YILw1nNTQtz3aTvFsO6O82oVX09vssd7o3Az+XVbDzuPV8V6K0E4xqeGtt0zygUuIIuHqLGeO6Y3bqxklFYHj7GCk7lhoTcLp9CffGUDXTgl0T08kK8VGpdPTxJbt0dVn3FYCZRKi5faBZJAvkKNV/v8BMpLaZvbiyj4ZvLbpIH/cdoTLunWK93KEdoiiYNTbBWdD5CRbiCaBOss/zBlNvUdDVaCixgWcybQt2bC3iRaD645bMrVREJpDuHrgdLslRHeuCLZsaQ31x4qihB0bvXRaASbVr2PRbdtBAuQL5JijHoCM5LaZQe6UYGFwj1Te23aER24ciEn+pxPOEwWF1zaGjvd9beN+fnHLoHgvTeiAVNT4hyIEmphO1dYaTVDbSh08/fEu5k8cRG5WEnar2QiCZdqY0FoE1wM31t34vGx+/r28sLZsdW4fCyblN4yJPvOBsM7lY//JWv77j19TXuMS3bYxpMTiAjkWyCC3kTHT4bi6XxZl1S7++e+T8V6K0A5RFZg5pjfz15QY059mjuktGWQh6gRPDHtofH/mrirmufV7WDAp3ziOLq9xkZlsxWxSIl4LMm1MiC1er8ZRh5PSyjqOV9WT1RADTCrIMVwuGg8OSbCovLZxP74GNzdVVVBQmPbK58x89Utj5PS9r2/hZK0rXv80oRGSQb5AjjicJFhUkqxtt25oaM90km1mVhcd5pr+WfFejtDOcHm1EHcBh9PDUx/t4nfNGBQSDeTY/OIhMDFsSE4a3dL8GbjDlU6e/viM/i5JszN/zTesKykLyRI3njY2JCeN2WNzqXN7Ka9GdCNEjcCo6dlB1peBJrw0uyXE5SKwZ+q6ztxVxUYGOUCkKXl1Lh9akm6cjsgeGD8kQL5AjjnqyUy2tWmHCItJZXRuBh99fZyqOg+piZZ4L0loR5hUpYm7QI90e6uU68ix+cVFoHFp9thcDlXUhZRWFC4voke6nVdnjqC82p8VDrbGCp42NiQnjYdvGNCkVll0I0SD4FHTcKYJb96EPKORNNjloke6neX3jGDh5HwURcHl0yivdpGRZI04JW//yVqSbP4SItkD44sEyBfI0SonnRPbZv1xMNcOzGZdyQne23aYGWN6x3s5QjvCYlL4/YxhHKmsJ9Fqos7to3t6AhZT7DfnSMfmkbxChfZNoHGp1uXlVx/u4IUpQ6is9Ri6S0+y8NRHO3jspoE8uXancSTt9vrolmo3mp5mj801gmMQ3QjRxRPB+jLNbuGTHcdZMq2A59bvZlJBDhlJVjKSrbi9PpJt5pAhNy/fNYx+WcksnVZAYZhs9AtThsge2AaQAPkCOeaoZ1D31Hgv45z0ykiiT2YSb31Ryt2je7XpjLfQttB1sKgKOZ0TURXQdDApOnr4qahRJdLxo3iFdiyCj5CzU6y4Esw8ccvldEows+hv/zbKKZ65bTDl1W4eenc7T37/WzyzbjcPjuuHqsLx0/WkJJh5p3AUoItuhJhhManGNLzZY3NJs1uoc/vom51E19Re1Lq8/Oy7efz6zyWGdhdPHUq9R+OZ2wbjcHpYsmGvEeh2S0tg/sRBZCZbSbCYqHF5eXBcP+xWE0637IHxRgLkC8Dl9VFe42qzDhaNuXZgNq/8cz9flToYcml6vJcjtBPMJoVql485jcb7drLHvlQn0vGjeIV2HILLaMKN310wKZ/yajfbSh089O525k3Io3B5EZdmJPLIjQNY9tl+7h7dO6ScYun0AsbnZbOupMy4j+hGiBbZyTZenTmc8mqXodXxedlkpvQ3po421u59b2xl/sRBzHz1y5AsceD0o2dGIidO14dmmKcPo0uqTfbAOCMuFhdA2Wl/l2lbHRLSmNG5GSRYVN78/FC8lyK0I+o9mhEcw5nxvvWe2E/SC2fKH+x7K7R/go+QZ4/NNQIOCB2wEPg54CVrUhTmripmUkFOk3KKwuVF/HeD1RaIboToYjardEqwhGh1UkGOERxDeO0mNjTzBx57cFw/LGaVilo3CWa1ifbvXb4Fr0+XPTDOSAb5AjhW5fdA7txOhJpoNTM6N5M/FR/lvyfkkdoKGUCh/ePVwh9Xe7XY11iEM+WXDu6ORXAZTXaKLazWcrOSGJKTRnmNi+xONl6/ZwSKciZgDneNSVVEN0LUCC4DspjVJsNAImk3reHvbI90Ow6nJ+Sx3plJ1NR7eXLtDh65cWDY6+s9PvplJYuW44gEyBfAsTY+RS8c4wZm88nOMv647Qh3j+4V7+UI7QBzw6jpxkd8ZtmghShgMZ+p50xtyA431lrpKSeP3DiARKuJn67cTnmNi0VThzI+L9twDQh3BC1NTEI0COem8/o9I0JcU9IiaDegz4WT83nqo10hj9nMKo9/8DV3j+5N6Sln2Ov3ltdS6/ZFdK0QC7jYIyUWF8DxdpZBBuiTlUyfzCTe/PwQemt0WQntHptZZdHUoSFHfIumDsVmjv22EfjDdOuizxiz4G/cuugzdp2oRmuF7LXQOphVhUVTh/LguH48uXZHkwELCybl89z6PcxdVUx1vddwrpjzxlYeu+kyVheVhh3KkC4nZEKUCOck8eTaHbw4ZahhS/i/YbS7ZFoB3+reibdnjaRnRiJZKVbjsZfvGoaqYJQINR6IE6z9SANvZH9sHSSDfAEcq/LbXtnb8JCQcFx3WTb/94/9bCt1MFSa9YRzUOv2sWLTQZbNGI5JVfBpOi9/uo/7r+tLZozvLRZHHR+n24cC5HS2GwMWlt8zgrJqFw6nh6c/3sW2Ugfgdw8IcLjSSZXTw6SCHC7tbGfZjOHUuLyUVbt4bv1ufnVrvmhEiArh3HTWlZTxo+v6MW9CHgO6pEQcDnLUUc/kJZuM5tH5EwehqioZSVYqat1kJFnDDsTJTrHx03e2G9oP51oh+2PrENcMsqIoZkVR3lUU5TNFUX6vKEqCoihrFEXZrijKcqWNepIdr6pvV9njAKP7ZGKXZj2hmZhVhfTE0M/Q6YnmVimxEJu3jo/VbOJkjZvSU34ngNljc/HpOnVuH+tLTjB7bC4rZ41k2YzhaEGnXj3S7ZRVu5i/poSSY9V859lPuXXRRgqXF7GupEw0IkSNgJtOMD3S7RytqqdweRHHT9eHDAd5cu1OrCYVu9VMqt3CkJw0o3nU0nDydqzKiY5O97QE47UD1z/07nZ2l9UYwXEk14pI+6PT45MschSJd4nFfwDbdV0fA3QDHgAO67o+GEgHvhPPxUXiWDsZEtIYu9XEqNxMPiw+RnW959wXCBc1KQkqE67owcxXv+S6Z/7OzFe/ZMIVPUhJiP22EekPk1gcdRwykqz0zEjkkjQbD1zXj/lrSrj+N58y7/2vmTaqJ6uLSrnjpc3Me/9rzKrCkJw0o8xn64EKlk4rYHVRachr9ki349N0CRKEqBDOTWfh5HzWl5xg2Yzh9M5IZPG0Anqk2xmSk8YjNw5g3vtfc/1v/PvlwzcMYEhOGlnJNo456o2SiO8v2oijzsurM4eHvPbiIE2fzbUi0v64t6xGSi2iiBLPelRFUZIBDXAD/wD2A6t1XV+tKMpPgSxd1//rbK8xbNgwfcuWLbFfbBAjfvVX8rp1ovCa3Fa9bzT4d1k1897/hv/9/rf4wYhL472ceBC3U4l4aLUlHKms4xd/+oZJBTnG0eHqolIev/lyuqcnxvTeMmoauAi0qmk6J07Xc9vSTSEZsfF52cy9YSCnat2G7v7n5sv55uhp4/vlG/dz9YAuIT7ICybl89rG/VJm0fp0WK02drGwmRUcdV7Kq12k2i2s2nKIob0y6J+dzPTff9Gk2W7ehDysJpV573/d5LE/zBmNgoLb68On6byx+QBDe2WQkWQlO8XGJal2zGF6PsLtjwF/5fIal5RanJ1mazWuNci6rtcAKIryOXAMyACqGh4+DQwId52iKLOAWQCXXtq6QZ6nYZZ6537tL4MMkJuVTE66nbe/OHSxBsitSjy12lIUhSaDGBZMyqc1Cp/E5q31iYdWVVVB00PtBIfkpHH36N7MfPXLEN0BFC4vAmDW1bks/ccBvjjgYN6EPDKSrKTaLTyyqphtpQ4ev1nKLDoyralVVVWMYFPTdHYcP03h8qbjoR+7KbxdW0aSlfSGeuPGj3m8Gt3TEymvdnHros/8z/nHAcAfQEcKdAP748pZIzlc6WxSsy9lRtEhamelDfXEP1AUpauiKHMURflZ4Oss12QoimIDRuMvqRgEBOY3pwInw12n6/pLuq4P03V9WFZWVrT+Cc2ivNqFTvtysAhGURTGDshm++Eq9pyojvdyOjzx1GpL0XWaDGJ4dHVxq4yaFlqfeGhV03QURQk5Lp49Njes7gKnxj3S7dR7fCydXsBjN38cI8wAACAASURBVA0E4Fcf7uBUrX9yWY90O3arifJqF0cq6yivdsmRcwcjXvtqRa3bCI4hdChIwNYtmB7pdrp0SkBp+L7xY4qicKLK31vx/A+GsHR6AUNy0ozXPlugq6oKVrOJh97dTuHyonPWLQvnTzSLCd8GBgJeYDpQ1PD92LNc8xBwm67rPqAO+BUwvuGx64C/RXF9USEwJKQ9T7MZnZuBqsCqrYfjvRShDdM4swf+TVtrhQhZbIw6PoH3+IkPvg6xucqIkG2rdXkNR4CUBDPz15Rwx0ubmb+mhEduHICm64ZP7YnTLtGOEHUiNcdlJFlZsmEvCyfnN6lXfvCtbTy5dgdLGmqVA48tmVbAa5/t49/ltdzx0mZuXbSR+WtKjLrl5gS6MnE0tkSzxOISXdcnAyiK8qKu6x8DHyuKMvks17wILFcU5X5gL/AKsFpRlGJgO7A+iuuLCmc8kNtvfU9aopUrctJ4b+sRHrlhICY5thbCoCrhB4WorVBjITZGHZ/g97i82s3r94wA/JZu4XTXKcHMe3PGoKo6E1/YGKKNuauKeWfWSN6890rMqspdQbWgoh0hWgSa4xprs0unBP6//xhEqt3Mylkj8Wk6e8treeqjM2UPAO8UjkLX/acmT3zwddhx6Y+uLmb+xEF0TU04Z6ArpWixJZoB8peKovwR2ACcVhTlx8B3gS8jXaDr+hH8meJgJkRxTVEnMEWvPbpYBHN1vyx+u34P//z3Sa7p376O/oXWQVFg6fQCyk67SLSaqHP7yO5ka5UaZLF56/gEv8fbSh2UV7t4cu1OfveDK3jmtsE89O52spJtPDiuHz0z/E2hmqbh9upkJdvISrYxe2wuaXYLmq7j0XSOV9WT3cn/WLB+RDtCNMhIsrJ0WgGFK87UIL84ZSjPr9/DO0X+E9lP545FB2a+6g99huSkGTrVdZ0uKQmcqK7nh1f1ITvljFaDn9c93U6XZFuzAt3gGmkhukQtQNZ1/ceKoozDX1IxEH+z3Yu6rn8QrXu0BY5X1WM1qyTZ2neNz5BL00mymXh/2xEJkIWwJFhUdB2j+zpwLJhgaT2bt3BjhIWOQeP32OH0UF7j4kilk0SriWdvv4IEi8p9b2xt4lLxxC151Hs0I4h+5MYBTP2/z43nBcb7Sl2mEE1UVaFbWgLzJw4ip7N/FPoTH3wTojMd2Fdea4xRf/iGAUaWeHxeNg+O68/soAB74eR83tt6hIlDuoc0RC+dXsBlXTtJNjiORLNJryswDnABc3Vdf7SjBccAx07Xk5FkpY3OMGk2VrPKlb0zWPvNcerc3ngvR2iDON2asZGDPws3e0URTrcW83tLbV3Hp/F7vLqolEVTh6IoCve/uY1TdW4jOIYzx8//ddNlJNksvPLPfX5Njs1l7qriJiUXD47rB4h2hOiSZrfSNTWBpz7aidWsUl7jAs7UFf/qwxKeW7+HF6f4x6gHl1BMKshpsqfOXVUctjG1cHlR2DHTQusRzRKLN4BlQBqwCH+jXoejvU7RC8dVfTP5ZGcZfyk5wcQrusd7OUIbw6uFb9LztkKzk9TWdXwav8cAv/vrHmZd04fDlU7S7Jaw+tOBkzUufjHxcn723Tzj942fl5uVxGePXivaEaJKQLe/ujUfTdOMmmOzSUVBZ11Jmf95in+MelayzRgjnZEcvgHVpCrnXVIW7M8sGo8N0TwrTdB1fYWu6y8AOVF83TZFe52iF44BXVPITLbyx21H4r0UoQ1iVkPtt8CfJWmNUdNwpraue3oiWSnNq8cT2hfB77HVbGLjvgr2NhxPR7LN2lNWw8Pvbqe82s2v/1zCrhPVYZ+XYDWJdoSY4tX0hlKhRC5Js2NSVf44ZzQbHh5LVoqNFJuZR24cYDiulJ5yhtWqqoTfa1VFwevVmlgWistP6xDNADlDUZQpiqJMBbIbvp+iKMqUKN4jrmiaTtlpF+kdJIOsKgqjczP5dPdJKhqOiQQhgM2ssmjq0JAyh0VTh2ILM9kpFmiaLl62FxGBkgu7GZY0jNwNtn8L1CAv2bCXw5VO5ryxlUkFOSzZsLfJ85bNHA46oh0h6kQKTr1ejYoaFw+8tY2frPyK/Sdr2V9RF1L+89z6PU2s4BZMymfFpv38fsYwls0YzspZI1k2YzgvThlCWXU9B07V8vP3ikPudbLWFdblR0oyoks0Syw2Adc3fP858J2G73XgzSjeJ26crHXh1fQOVcs2OjeDD7Yf5c//Osb0Ub3ivRyhDVHr9rFi00GWzRiOSVXwaTovf7qP+6/rS2aM7y2jpi8+AkfXnRLMHHXU84MRPemUYGbZjOGYVYXdZTUh08ICZRjbSh08/fEu5k3Io192MidO1+OodTNz2ZeiHSHqRLKgfKdwFPc2DBGZNyGPuauKeea2wSGlE9tKHTz10S6W3zOCsmqXMQEP4OYreoQ0RD9z22Be+ec+fjCiJ3NvGEh5tX8Qzr2vb+HNe68Ul59WIJoBcgIwGPAB2/APCtkKHIziPeLKGQ/kjhMg98xIIqeznT9uOyoBshCCWVVITwzdItITza1SYiE+yBcXwfWUXk3HbFKY+eqXhvVVblaSf5T0jQNQFQWH08PqolIcTg/gDzzmrylh3gR/TfL8NSWiHSEmuL0+o644O8VGWqIFq0nF49MMzQXq5zVdZ9mM4SRaTWi6jk/TSbCY8Onw5Nqdxoe9ZTOGN2nee+jd7cybkEei1USV08PDNwwwPiCaInjUi1NLdImmzdsPABRF6QzcAcwAngV2AZdF6z7xpCMGyACjczNZ+WUppafqyOmcGO/lCG2EZJvKhCt6MPPVM5m4xdMKSLbFvsRCfJAvHoJPC7KSbSydNpRUu4XxedncPbp3iPXVwsn5PLl2J+U1LhZPK2DNV37v2UDG7cm1O3nspoGiHSFm2K0mHrlxgFE6ESiTsAYNuHE4PYzPy0ZVFOa9/7VhRfjYH/7VxIqwvMZFz4zEiBP6quu9uH2a8QFw/poS7FYTL981rMkJW0c63W4LRNPmbaWiKDuAvwOjgbeAa4Ebo3WPeHP8dMcMkMfk+g/M3/9KmvWEM9S4NO5rlNW4b0URNa7Y27wFPHKDkQxJxyT4tGD22Fy8ms6Ta3fw2E2XNbG+ClhiBbQ4bVRv1j90DSt+eCWv/HMf20od1Ll9oh0hZng1vYmt4KOri3F5ffzuziv8dm8b9vLYTZfx0Lvbz2pF+Js7BjNvQh5HHeGb9zonWUlPshh194E6/TS71XCA+ezRa3lvzhgpIYoB0UwF1QGbgS2AF3+5xQzg8SjeI64cq6rHpCp0slvivZSokpViY2DXFN7bdgRdl2YWwU9Lbd5a0mQnPsgXD8GnBWl2Cx7Nb5VV5fSE1V9aw/57uNKJy+vjZLWLY1VO7rmqD3+fO5a+2UksbtRcunR6AZqmScOe0GI8Xi2sLi0mlV+u2cGb917Jb++8AnOQdVsky8Ky0y4KlxfxzLrdTRpNl0wrwOPzUVPv5bGbBrJsxnAu7ZxoBMLNcfmRRueWEc0Si5nReq22SsADWW3nQ0LCMaZvJq/8cz/fHD3NoO6p8V6O0AYI2Lw1rnNrTg1yNJrsbGaV+RMHGWOuW8s9Q2hdgifqabpu6K6s2hVWf9kpNpZO97tc2MwmslJsaDr4NB8vfvJvJg7pzmsb9zNvQh5dOyWQlmjhVx+WsK6kTBr2hBYTacpnYBLk3rJaAHplJoaUXIS7JivFxl9/ejUJZhUdWPHDK1EUKK92YTbBqVpPSFnG0ukFdE5snt+xNDq3HPmLcx50JA/kxozsnYFZVfjDVimzEPyk2VUWTysIyWosnlZAmv3c20akJrvm2hCdrHVx1++/YOarX3LHS5uZ+eqX3PX7LzhZK3aEHY3AaUGgZnPrwQoWR7B5Wzg5n5++s535a0r40bj+rNi0n+ue+Tszln2B2wd3j+7Fo6uLWVdSRuHyIo6frmfq/31uDG8QOyyhpYQ73VowKZ/VRaUsnJxPTmc7A7sloygYNplLNuxtYu+2eFoBT67dwdx3iymtdPKDlz9n7NMbmPp/n+PyajjdviZlGYXLizha5WxWJrile7AQXReLDs9RRz3dG9UJdRSSE8wMvTSd9786ws++OxCzST47Xew4nBrPr99tTIFyOD08v343j998OUkJZ7+2pU129Z7w19d7Yl//LLQuAXu3J24ZxO1LN3G40sk/Hx3L4zdfjqLAylkjcft0Dpys5amPzti83beiyO9a8Y8DRk3yylkjQ3QT6WhbGvaEC6XxBEhFUVDQ+Z+bL8dqUumcaOVUnZv95TV0S7Xx5r0j8WkaJlVh5ayRlFW7SLaZWfjxTtaVlLF0ekHYmubl94wIX5ZR7cJuNZ/TkUUanVuOBMjNRNd1jlfVk9+j45YfXNUvky8OnOLTPeVcN7BLvJcjxBlvQy1oIPsW4OffyzvntZGOIZvbKBXJxsgkJ4MdElVV0HV/zfuQnDQqaz3c98ZW42j4tXtGMPPVL0OuCa5HDvzs1fy2Ws+t38O2UkfEo21p2BNaQqD+NxyaplNW7QrxNF4wKZ+nP97FYzcN5I6XNrNy1khjX430Ic6n6yHaHZKTxoPj+pGeaMXt9aFp+llLJVq6BwsSIDebU7Vu3D6tQzcJDclJIyXBzOqiIxIgC5hVhfF52UwqyDEyyKuLSptVgxw4hrxQG6Ikm4nFU4eGBEmLpw4lyRb7zT3Yk9dqNpGR1LyaP6FlBP6gzx6ba7zv4A8WDlXUhQ0WMpKtLJ1ewJINeymvcRn9IU/ckscTH5SwuqiURVOHMidIR0unnWnYa857K3oQmoum6Rw/Xd/E0/jR1cU8fdtgo+bYFhS8Bj7EZSXbmD02lzS7hTq3j04JZt6eNRK3139q5nR7KVyxNWQ/DVdPHNCrpmksnV5AYcPwEml0Pn8kQG4mxxo8kDOSOq7RvNmkMiY3k3Ulx3HUuUnroPXWFxMt+eOebFP50bj+htXb+fggNz6GPN97u706z3+yJ7S845M9/PI/vtWs6y8UaWxpXYL1aTGpvDpzOI66pu4Vz63fY3xgCnjKBvvQLpycT6LVxINvbaO8xsXCyfksnjYUt1fjjc0HLrhhT/QgNJeAVmpd3rAZ4S6dEpj+yuccrnQyPi+bJdMKmL2iiCUb9vLilCHUBdUcB1wsnlu/29Dqwsn5ZCXbOFzpNOqJ/zBnNNkpCU3WENDr+Lxs3vx/V2JSFflwdwFIoWkzOerwC75zcscOGq/un4XHp/On7UfjvRShhQQ2y1sXfcaYBX/j1kWfsetEdbOtflrqg9wcG6JI1Ht9RqPVHS9tpnB5EetKyqj3xrYGWRpbWo/G+vz+4o1U1rrpnGRt4glbXuOixuVl3oQ8fnPH4LCestX1XraVOoyfvZpOj/RE/t/VfRl0SSeyUmzn3bAnehCaS0ArFbXusJ7GB07WGjpaV1LGc+t38/askfz2zivITLY10fTsFUVMKsgxfg54gAcI15PRWK/rSsqY8n+fY21we5Hg+PyQDHIzOZNB7tgBcu/MJHpmJLJyS6mMnm7ntHRcs1fTjZGqgSzukg17m+2D3BLiVYMcPEY2+N8sjS3RJ5w+f/LOdhZOzueFKUOorPUYFn/pSRbe3HyIcXld0HWYNyGPJRv2Gg17AR/aAAHLuOBa0SOVdefdtBSLRicp2eiYBLSyZMNeFkzK57WN+7lrVC+6ptqxmVVO1tQzJCfN0Oy6kjJmXZ1L19SEkDHVAcLV2Af/HG4/vBC9ih4jIwFyMzlWVY+5Aw4JCcfY/tm8tukA3xyt4vJLOm5TYkenpX/cE8xq2KPshFbwI06wqiycnN/03tbY3jvcGNmFk/OxW6WxJdpE0meyzUyd2xfS5LR46lAmFfQwJpMFNz5tK3UYPrQBeqTbUVBCGpksZjXshy7LWfQc7UYnKdnouAS0sq3UwfvbjvDAdf1Cat8XTs7n8Vvy+MUHJYZmKxpOTICI3srBP9e5fcb3z94+uMm+dL56FT2eHSmxaCbHqpwddkhIY67qm4nFpPDOl6XxXorQAlo6rlnTCXuU3RrDmBQUEq0m5k8cxMpZI42BIQqx/f8v3BjZwHG9EF0i6dNuNRuBMPjfg5M17ia/e3S1/8g5EECvLio1XuOZ2wYzf803IaUQZlVp4kW7cHL+WZtOoz3RUUo2Oi7BWhmX18UIjuHMPlJZ6zE0G/BOBnhy7Y4mnt8BL/DAz0umFdA3O4n35oxm/sRB9Ei3k2a3RlxD4Lqz6VX0eHYkg9xMjjqcZHTw+uMAyQlmhvXqzHvbjvBf372MBItkz9ojLXWScEc49nP7Yu9F7HT7eOKDEmaPzSURE26fxhMflPDClCGQFLv7Rhoj64lx7fPFSCR92sxKk/cg0WoK+770y05m3oQ8lm86yKSCHB676TL2lNXw5NqdbCt18PjNZ05LnG4fT320K6R85qmPdp1VUy1tNm2MeNN2XIK1UucO36iXaDXRMyWReRPyeG3jfn54VR9qXV7WlZRRXu0O0WayVWXuDQP5+ffycHs1Xv50H+8UHTZe77NHr22iw/PVq+jx7EiA3EyOOurplRnDv8xtjOsGZLNpbwUff3OciVd0j/dyhAugpX/cWzJquqVYzSYmDu7KZd06oek63dLsTBzcNeYenuId2npE0megySn4Pahz+8K+L3vKaihcXgTAxn0VLJsx3KhNbvy+Wc0mymtcxvMDr2E1m9A0nZO1Luo9PkyKgt1qopPNQqXTY6ytW6q9xcfOoq+OTaDmvbw6fMlEnduH1ayS160T+RMH8eo/9zG0V4ZRmhHQZsD7W1H8/RiOOg97ympCXiugmRAnGLOKWVWavd+LHs+OlFg0A03TOXG6vsM36AWTd0knslNsvPXFoXgvRWgBLXGSSLObIoyajv3mmWIxUdA7kykvb2bswg1MeXkzBb0zSYnxaUa0j9SFsxNOn+Heg/QkC8/ePriJFrceqDB+XjApn4Uf7+ThGwYwPi+7yfuWbrewpJGel0wrIC3BzK4T1Xx/0UaufmoDd7y0mV3Hqzlwqpafv1d8QQ4wkRB9XRyEe58XTs6nc5KFH725jR+8vJkDJ2uZNCyHrQcqmpZXTB3KgrU7uP43f+fOlzfj1TSeuCWPITlpIZpp4gSzaCO7jlfzwJvbmqVZ0ePZUXS9fdfWDRs2TN+yZUtM73G8qp6R/7uee8b04jt5XWN6r7bEH7cdYeWWUv728Fh6d5zsedyKyFtDq41pSYfykco6Xt+4n8nDLsWkKvg0nVVbDnHX6N50T0+M6bqPVNZxx0ubm2Q2Vs4aGfN7t6Gu7otGq43/m6fbLZTVuDjqcFJR62bJhr0APDiuHzmd7ewtr2V1USn/c/PlHK+qN54TyBy/UziKrp0SQt638moXP3+vuMngm+AR1wF6pNt5+rbBVDk9IVm95jrAnM+/tYO4Blw0Wm0uZ04lNFQFjjnq+fWfdxguFj3S7cyfOIh+XZLRdR1VUfzOK4rCL/70TcgE08Bz+2YnoyoYpxnl1S5uXfRZE+3Om5BH4fKiZmm2g+rxbDT7HyclFs3giKMOgMzkjjskJBxX98/i3aJSVhWVMveGgfFejnCetLRD2avpLP3HAZb+40DI76eM7BWbBTe6d9jxq63QLHe2MbJC9Imk004JZiYv2RTy3JmvfsnKWSONoPXn38sLCY7BrxNdbzqG193grd10dHr4uvOsFBtJQS4B0arNFH1dHKiqYgzxOFJZx21LQ7UcqEn2aTr7ymt5bv0eymtcvHbPiCYaDTzX49NISbAY2o5UQ5zdoK/maFb0GBkpsWgGAQFebAFy5yQrg3uk8e6Ww3hboTFLiC4t7VAOjJpeOr2AlbNGsnR6AePzslulBjlQ/xxMj3Q7po6d2bgoiaRTRQnVwJCcNJbNGG6Mlx6fl83O49XMX1PCwzcMYEhOGhDZus1qNlH47V785SdX88lD1/CXn1xN4bd7YTGpYbV2qKIupEFZajOF80HTdMqrXRyprGuiZThTk7zzeDVvfXGQZ++4gsXTCtB1GJ+XHfLc8XnZZCTbMKkKHp/GiSon5dWuiK+bbDMb34tmLxzJIDeDow7/kJCL8VPW2AHZPPvX3fxjz0muHZh97guENkNLO5Tt1vCjpu0x9iIGMKlK2GEREiB3PCLp1KRguFyEGy+9aOpQVmw6aFi+zZuQx/w1JSycnE9NvZfMpNAsclqCmQlX9GDmq1+G6Dkz0cLSaQX8bv1uJhXkkJFkpXOSlSUb9jJ1ZE9AajOF8yPcyOel0wooDNpLA/7qb24+xN2jezOtYQx1QJfgHyYyPi+bB67rx4xlX4R4gL+2cT+P33w5Cybl8+jqYuM+j910GSZV4Z+PXovNrJKWIGHehSL/5ZrBEUcdKTbzRWl3NvTSNDolmHm3qFQC5HZGSzuU69zhR02/PWsknZtRkt6S2jYFv+Va8LCIZ28f3CqFjhdhTV5ciaRTVVUNlwuX18edQTXphyudzHljK8tmDGdPWQ3bSh30y05m2YzhPLKqmPIaV5Pay/Jad1g9v1s4iqwUa9jBDt1SE/js0WvPqQPRjBBMuJHP9347l6dvG0xmshWb2cThyjp+0WBlGQhw4Ywu37p3JD/7bh4WkxLSjxH4QLhsxnBKK528tnE/8ybkcUlqApoOd/3+i5APkV5Nw6SoeHyaaPM8iWuJheLnNUVRNiuK8oGiKMmKoqxRFGW7oijLFaVtTOU4Uukk8yLMHgOYTSqjcjP5a0kZVXWec18gtBla2qHsa0EdcOPu6vN1AfBqOj95J3QwxE/e2R7zgR0tXbdw/kTSqUn1D2gC0PTwWlQVhcdvyWN8XjZ7ymo4VetmW6kj7EmJN4Kvt8ense9kXdjBDl5NP6cDjGhGaEy4UxFN17nzpc1c/5tPKTl2mrmrigHon53MM7cNZun0AqNM6HClkxOn6xn79AaOVdWHP2FRFbp2svHz7+WxuqiUo1X13P/m1iYfIt1ene8v3ijavADinUEeA5h1XR+pKMoG4B7gsK7rExRFWQN8B1gXzwWCX2gX89Hat/tl8vE3x/nwX8eYcuWl8V6O0Exa6oNsNYUfzWs1nftzdaS60ua6AHg1naxkW4hx/pINe2MeILd03cL501inFrNKTb2XW174zDg2/vn38sJq8dCpOhIsqlFeMakgx3jMp+kho6ZNDTX1jV0sVNU/nzFS8Hyu7LBoRmhMuFORYC/vJRv28sKUITjdPqb/PrR04umPd1Fe4zJ6RSpq3WF1e7CijpmvfkmPdDsvThlKWqIlrIbLq12izQsk3k16J4DfNXzvBp4A/tLw8yfAtXFYUwi6rnPE4bzoGvSC6ZOZRPd0O3/YevjcTxbaFC3xQVYVwo7mbc5LtLj+2azyyI0DmL+mhDte2sz8NSU8cuMA7GGar6KJ2+sjK9kW0piYlWyTyVIxJlinCopxTAwwqSCHX31Y0sQrdsGkfJ5bv4e5q4rxaTBzTG+WbNhrPPbLD0tCGlItJoUHrusXoqkHruuHzawawUswgabQc2WHw2k9oJkjlXWUV7skY3eREe5U5JK0BH535xXGUJB6j9ZkrP2jq4t5cFw/FkzKN6wN15ecCKvbtf86Zlx3/5tbIzbsNW7KFm02n7hmkHVd3wOgKMqtgBUoAqoaHj4NDAh3naIos4BZAJdeGtuMZpXTQ53bd1EHyIqiMCY3k3e2lHLE4aR7mv3cFwlA62o12tR7tbCjeX975xXnvLal9c9enSZ/POauKuadwlEX9o9pJhaT2qQZbOHkfCzNyJq3d9qKVhsHnGl2izGKd/k9IyirduFwenj6412GtVugGO+xmwaGPBY8atrj05uUUcx5YyvvzBpJTmf/+xz8vj97+2AsqhI2Oxzss9xY60Ny0njkxgFG3ej52isK56ataDUSgVORlbNGcrjSicPpwe3V+OWaHTz5/W/RNTUBi0kNm0TIzUrilx+WGNoel9clrG7nTcgzRk8frnRS5/KwaOrQkDr6JdMKeG79buP1RZvnR9x3fUVRbgF+DNwMlAGpDQ+lAifDXaPr+ku6rg/TdX1YVlZWTNdnWLylXLwlFgCjczMAWLP9aJxX0r5oTa1GG7OqkNVI91kp1mbZvLW8/jl8vahPi63doNfXNKszd1XxRWFz2Fa0Ggg4AzicHuNnr6Y3cTIJlFMs+2w/DqeHNLuF2WNzGZ+XHfKBLFIds0+HLp0s9M1O5u1ZI/n73LGs+OGVdE9PwKfD6/eM4C8/uZrbC3oY1xx1OI1McmOtPziuXxMNnY+9onBu2opWz0bgw9Mr/9wHgN1q4sFx/Xhm3W6u/82n7DxeHTbjazGpzBzTmx7pdobkpNEvOzmsbtPsFuCM/WGCxUxaooXVs0exYe5YXrtnBEk2/2sF7Dp/c8dg0eZ5ENcMsqIoXYG5wI26rtcqirIeGA+sBq4Dno3n+uBMgJx1EWeQAbp0SqBvdjIfbD9K4TW58V6O0Apk2K1hbd4y7OcOclta/2yJUP8c60yuJ0JjYqxrn4UzBALOQOZ2dVEpr84cTnm1K8SiLWB19cOr+vDprhNNtLpkWgHpDUEEgM0S/lTDZlaprPPh9emcqnVTUetmdVEpPxrXn+fX72ZdSZnhCACwcV8FFbVu/nPlV0YtZ5dONuZPHESi1URGsrVF5UVCxyHdbuHBcf2Z3cje7amPdrG6qJQl0wpCHnv5rmFkJdtwenw8e/sVJFhNHKyoC6vbOrfPyAg3PvHKSLZS6/Lyo7e+YnSfDB4aP4Cjjnp0HeZNyGsyWEe0GZ54N+ndDXQDPm4wrFgOdFcUpRjYDqyP49oAOHSqFvAHiBc7o/pksHzzQfaV19AnKzneyxFizKl6T1hbrHcKR3FJM7w1WzKhyawqTY4LF00dGvMhJYEBJY3/GIn/cusR7sOV16cxY9WXTeo1V84aiVlVyOmcGDIu+nClk9krses4kQAAIABJREFUikKakTKTbCGBd7BbhqPOE6K1BZPyeX79bubeMJAfXtUHh9PDC5/s4ZEbL2PikO48/fGukMDC6fYx89UvAVg6vaBF5UVCx6HS6TECYDhzIrVy1khjpHq4JEKvjCQSLCZuX7qJrGRbiNdxj3Q7S6cXkJFk5fkpQ5rYH85dVWyMpc5KtnH/dX057fSEWGYGmgEDo9lFm+GJdw3yAmBBo18vjcdaInGwoo5km5kkW7w/S8SfK3t3Zvnmg6z9+jj3X9s33ssRmkFL/Fk9EWyxWqPcoN6rsfVABW/eOxJd11EUhU9KjtH58m4xva/FpPLSXQWcqHIZA0q6pNouihrkeBBJn40/XB2sqI1oOdg9PZEjlXXnzNqqqkK/rGTeKRyF16dhNqlkJ9s4Xl3PnDe2MrpPBvde3QeTqqDpcP+1falyerjjpc1GRjrFZmLtgVNNAotAWUhWsn889ev3jOBgRZ0xPvjl6TJk5GIhWNPgP30O1qZxKt2g78ZJBK9Xo6zGZey/Wck2VAWW3zMCn65jNakkWk1ogM8bfo/umZGIzazw2zuvwOPTOVnjNtYR+HAZcH5ZOq0Ai4kQxxfBj0R95+DQqTqyO13c5RUBMpJt9MtO5s//OiYBcjug8TSn823IiGTz1hrBotWkMLRXBlNe3hySQbaaYruBp9rMlFW7QrItS6YVkCofkKPO+ejzXFpsTlOopunsKa9pcr9km4nRfTKYNqpnkyl7FvwfBgMZ6fkTB3HNwGxqXB7+Y2iOEfRmJFl5/Z4RnDhdH3LcvXjqUOo9GhazIgHIRUA4TQdKKgIlDWfL2Hq9GjtPVDN7RRHzJvj9ve8e3TtEU0umFeD2acxY9iXzJoS3P1QVheOnXU1ORQJZ48OVTvplJzNvQh6/W7+bmWN606VTAr0ykkSjQUha5BwcrKijS4qUVwQY0bsz3xw9zaGKungvRTgHkfxZm9uQYbUoYW3erJbYb6Aen84Ln+xh3oQ8Vs4aybwJebzwyR48vtjWAp+sczc5Ep29ooiTddLEEm3OR5/n0mJzmkIj3U9VFB4Y15eKGrcxsCEr2cZ9K4pITjhTw3y40kmi1cScN7Zy1+jeTQJ5u8XUpAHqvje2kpZo4amPdlJW44rmfz6hDRJOY3NX+a3b4NzNymU1LmP/WbJhLz/7bl6TKXuzVxRResppPOfFKUOb2B/WurxN9Pzo6mJmj801nrenrIbC5UWsKylj7qpiDlbUcfx0PVqDf3h5teuit4KTtMhZ8Po0jjqcDLk0Ld5LaTNc2bszb3x+iLVfH5NmvTZOS72I693hbd5emDIEmjFquiUoCtw9undI3d2CSfnEeramtwXTA4Xz43z0eS4tNqcpNNL9LCaFyurwNZrBcuuRbsfh9Bh6CLx2IGtY6/KGff0qp4e7R/dGQTTU0Ymksdzs5GaNLA8ua9tW6kAn/H6UaDUZz1EVQv6/eH/bEaaP6hlWz2l2S8jPjV/zqMNJvceHy6td8MljR0IyyGfhWFU9Xk2XDHIQWSkJ9M5M4uNvjsd7KcI5aGyXBefXLGQ1myivcVG4vIg7XtpM4fIiymtcrdLQoes0yZw8uroYPcYxRqBJLxhp0osN56PP5mjxXENxIt3P49ObnBoEBjYEPhcFgorAIJJgPVTUunn2L7tIT7JGHNTw6Opi47UkO9dxiaQxu8XUrGFNAfeeAF6fHvb16txnPkQerao3hogULi9iXF4X7mvkmxzQ8yVpdt6eNZLXNu43Sj4Cr3lJmp2sFBunnZ6znuxcTPqVAPksHGwoI+giNcghDOuZztZDDspO18d7KcJZaKkXcUuvbwmRPGu1GEfIFrPfPSP437xo6lAsZgmQo8356CsaWoz0Groefsx0r8xEOiWY+NvD1zB/4iBjBPDiaQXYrWf+dGqaxg+v6sOCtTvCTvtbsmGv8fqBbPPZJvMJ7ZeW6jQ72caSaQXG9au2HGJx0M+Buvacznbjd6uLSlkctGdlJIW3GeyVmcTz6/cAOj8e17+JTuev+QZHnZvUCCOr3V7fRadfKbE4C4dO+QPkbLF4C2F4r868W3SYv+w4wdQre8Z7OUIEWupF3NLrW4KqhLdbU2NcY+Hxnql9DhxZvvDJHh6/+fKY3vdi5Hz0dT7PPZszRrjXqKh1h9VaVZ2H3SdqWF9ygnuv7sMztw/GbFJ5Y9N+7h7Tx3iuT4eH3t3O4Uon5dVu5k3IIyPJSqrdwiOrikMcLyLVQQfb0Qntl5bumWazysAuKSFOK5mJFt4pHIXHp2FWFZJsJpKt/t8ddTipqHWj6brhw901NSFCQ6vCxn0V3OfL5bWNB3jr3ivxajo+Ted4VT3l1W5O1Xo4VeuJ2PB6selXAuSzcPBULWZVoXOi2PME0yPdTtdOCXz89XEJkNs4LfEijsb1F0qiVWXxtIImQ0oSrbE99PJqOutKylhXUhby+//+Xl5M73uxcj76as5zvV6NXWXVFC4vCls/Ge41MpKsLJ1eEHLNwsn5JNpMrN5QyrqSMjbuqwhxAbhrdG/jej3otGNbqYPC5UUArJo9ygiOA1nEY1VOGSLSwWnpnmk2q1ySFlpWcYm1aajWtVMCVU4P/7nyK565bbDhw/3Rj69q4pscaNx75rbBVDk9OJxuHHUeoxQj8JxOCWb+5/1vmvouTyu4KPUrAfJZ2FdeS7fUhIuuMP1cKIrCsF7pfPT1cU7Xe+gU1OktCNGgzq3x/PrdIZnc59fv5vGbLyc9hg2CZlXhiQkDuS6vG5quozb4L0sNcttH03SOVjmNQBeal+FSVYXuaQks/+EIVEVpOKXQWV9ynP+ecDmzrs6lotYdcbBCsAfy7LG5pNkt1Ll9dE+38/e5YzGrCtnJ/tpTRVEYn5fNpIIcQ9eri0pRIpyMBHviWhp8m81mqYwUQrPVmqaxbMZwEq0mTKrKp7tOsGzGcEyqgk/TWbXlELcN78mTa3cC8NTkfMPSEM7UKb86cwTlNS6e/vhMQ6yqKKQnWjjiqMOsqrxbOApN13E4PawvOcFN3+qGt6EuOd1uodLpafUTx1ghAfJZ+HdZDd0afZIT/Azv1Zk1xcfYsKucWwZfEu/lCB0MTQ+fyZ03IbaZ3DS7SkHvzBD/5cXTCkizS1DS1qmodVNW7TrvDJem6RyrcoV07b9ydwEFvTP55ZpvuHt0b+avKQnxoQ0eYZ1utxijsIP9ap+9fTC//vNOymtcLJlWwMAuKVhNCg9c16/JhMhw/t7BnrjB9x7YJUWCZAHwB8kZSVZ2nag2XCsKv92LCVf0+P/Zu/f4uOo6f/yvzzlnZjKZ3CaTpLekV0ovYApNQAWUmyAqim4L8qUFQRcsqLuyLrB+v+r6+7ruftmu6wVta+uKQGG9tLq64CpCKSAFIWlpgJbeL0naJmnul7me8/n9MTmnM5mZXOee1/PxyKPJmXP5JH3nc975zOe8PzE1vVVFomPAb1VWifd7oiqwlr/+/BONuH55Fb54zWLcsvm1qHdX/vUPB1BZbI+K5euXV8Usq53r1S+YICcQCBk42TmEFdUs8RbPeVVFKHPa8Md3zjBBpqTL1BzkHq8Rd3ntX9zzPrj4KEJWC4T0hPOJR6u8Em9e5amec4vFRM4rriopwD89/Q6+/alaa0S62xvE2YEAHn3lWNQ7HltePop1Vy3C559oxLrhJdoBxJ3j/s1PXBjTrsiauGa7zPOMfAuepq+R8btyviduH/bYZy+14q6s0B7ze3L98ipoigJF6PjFPe/DgD+EQruG/7UldinrJz57KUKGjBqFXlVXExOvuT4/mQlyAie7BqFLidllvCvGowiBunluvPBuO3xBHQU2ruVOySME8L1PX4Qv/+JNazTie5++iHWQKSG7pmJ7Y3Ps/Mnb60atIhCvdm2hXUVlkQNfv3E5qoodKHJo8AV1CAAd/YGoEelASIfHZcMXrl6MLzwVO6cTOLdEu0NT8HfXnw9NUaGI8Oqkf3f9+TAHkCMfMEwUiyHdmNIS8pQfzBgYCkTX364qdsSNG00RcNlVFNgUFNoVa6TYHP390rXnW8mw+W5FompCupQxr5U5E1e/yFVMkBM43D4IAPxLfRSXzC/H8++245XDZ3HtshmZbg7lEbuqoMSpWU9mDwV0lDg12FO8zLVZB3nkCCTnIGc/j8uO+69bgu/+6cC5Ed9iB2aXOkdNHuMtU60IgQdvWBI1ZeLhVbV4ZMchPHjDEjjt5wYEnHZ1eDQtehT6oe1NeOyzlwI4tyy2pgoEQhJ//eTrUVMsNFXELFP88oNXJ4zFqSwhT7kvMlYil5u+uKYMpcOLgYyMm0DIwOOvHseXrlmMW378GiqLHPjWTRdifkUhpATu+OnrMe9WPHrnJXHP1dzlRWWxA9cvr7KmwfV4E1e/yFWcyJTAkY4BAMDsUibIiVwwuwQuu4r/eZuLhlBy+UMGPvuzBtz1szfw6c2v4a6fvYHP/qwB/pCR0usqAnFr2TLvyH7mQ0vf/lQtLpxdgnkeF6rdhWPO141Xu3ZmaUHMstEPbW/CqroaPLCtCaGIdxTMB5TijZ4JnFsWWxGAL2jEXULdFzRi3irXVMSvya2KKS0hT7kvMlYil5ted9Ui/L849bg3ra3DlpeOYlVdjVW5Yk9zD+762Ru4/T9eTzhS7Avq+M7NK2L6wx88fwjrtjbiHz6yLLoe84iazemqm58qHEFO4EjHAMpd9qiRAoqmqQpWznXjT/varKesiZIh4dvLKZ7qoEvgsV3Rc0kf23UM32Ad5JwwmRJb8WrXjnzbGgjHn/k2cjDiD7VgyEg499mmKvj6jcvxr384gO/dehFsqki4hPrIqR6+gIFn9rbGVCO4/bIFefdWNk1MZKxELje9uKoIz+5rt+bNm31YWaGGXza2YFVddfyH8xI889HrDaKoQMMTn7sU7X1+9HiDVjUXAOj1BqOuM6vEkZG6+anCjCaBw+0DnH88DpcuKEevN4hXj3RmuimURxIt+ayluLO1KQJ3f2ChtXTrt57eh7s/sBC2HO7kaWwjl6lOFH/m28gjy7yZc59HjvZqqsDCChcunV8Gp02FlECRQ8Ojd16C//7i5fj6jcvx2K5jkDJ2meIzfT58tHb28IOC4RHqj9bOhn3EcsQX15Th0TsvgS4luod8aO0ewonOQbR2DyEYZNKcbwxDQgiBbevejx/fXoeLa8qs5aYPtQ+g2u206nGbfZhuAFs/dylmlBTg0TsvwcU154oPVLudONPni4nfH922EvM9hfjiU3twsG0AX/nVXqvG949vr8O2de9HkUPDpp1HrOtoqkBQNxAyJILDc+VzGUeQ49ANiYNt/bjq/KpMNyXr1VaHO/6nm07hg+dXZro5lCcqXfa4C4VUpvjtOodNoMxlj5r7XOayw2FjgjydVBU5YuLv4VW1eGzXsZi3jd1OG/722vPx/eG63R6XHeUuO4K6jvu27kbHgB+P3nUJzg74cc8T0efb3tiMz1y2AOpwua4td9Rbb53vfLcNH7+o2qqmEVliztyvsshhzZW+bKEHa98/L6qE3Ma1dVhaVQQbH6LOCyPnqZtx9NKBNmxaW4cfPH8w5iHVR++6BF2DAfzDr9+KKdXWMeDHhjUr8cMdh9DRH8C3broQCypcUBSBf3r6HXzuioXWNA4z/uO9A/LYrmP42o3L0dLty6uyhELK3M7w6+vrZUNDQ1LPebh9AB/69xex7sqFuJJJ8pg27DyMN5t70PC1D8GR/RPyM5bppCJW81kgEELHYAAhQ0JTBCpddtjjrCiVTK3dQ/j05tdi3mr8xT3vwxx3YUqvHQdjNYOCQR3tA37ohoSqCGiKgKIoMW8bd/T7cfxsuOpRRZEdDk3FmV4f/vn3+623oh+98xIr0TVVu534+o3L8a2n91nxFVmdQgiBW378aswxv7nvcmuJ7EBIt+L1T/d/MKrslrl/mmKXsZoGHf1+fGrDKzH/x7/8/PtRVeRAtzcIwzCsJaQ1VYGmCPzVxl0xxzzxuUthVxW4HCoCukQwZMCuqZBSWvv/+PY6qwb4xTVlMQuMmOcypwFFPugX2bYsK3Yw7ljlCHIc+073AQDme1K4ZFceuWxRBV4+dBYvHTyL65azmgUlh92uYU6KE+KRMjX3mbKPzaaOK7EMhHQYUuLWza8BAH5xz/vw6eHPTYV2ddQ5zWYZwcg51K3dQwnnGpv7Re6jKoKxm+filSRs6fZCSglNU+LOv08UR+19flS7nSgrjD7mROegtb85cvzQ9ibsae5JuMBI12AAlQnKy4X01D5YnUpMkOPYd6oPmiIwJ7v+6slaF84pQUmBht/saWGCTDkt0VLTqZ77TLkr/FCfbj3k1OMNxiwnLYGoUlzrrloUTk6cNvzhyx+AqgiEQkbUW9F2TcXnPzAfq+vnRj2kN3L+s3kt2/Dc5JEjeIzd/BGvJGG12wkhwmUC4z0Ql+iYWWUFkAgnxDZVgUMT8AXDyeyjd16CHzx/CHuae/BvfzyAb910IRZVuQCI+MuqlxVAQkSVfTOvoyqJ25bt9byZIMfxzqnecMfCqgzjoikKrjivAs++04bOAT88Rbm5ag6Rx2mPu9S0x5m7pYootUodKqrLw/M6H9jWhOf3tcUsJ/3oXZdg8+11+N5zB/GZyxZYczk//0Ti+ZqlDjXuksGljnMJsttps5b3rSxyWG2I3L+K/XHeGDlP3ZwD/M3fvY37r1sStxZ2vGN+emc92np9uP+Xe8/FypqVeGTHITy7rz1mnnJ1uRNtvT5sefkofnjbxfAG9Kg4+87NK/Affz6KL117PgBY59iwZiUe33UMn1xZE9O2ePOps62eN+cgjyClRN0/PYf3zCnFuisXJe28+a65awgPbm/C1z62DH/9gYWZbs5oOFeOEuIc5DDG6vi1dg/h//vvd3DX5Qsws6QANlXBrVtiY+jX916GoG7g05tfs+YejzZfczyxOHJO6sU1ZfibaxdjYaULmiJQVeRI1wN6jNU0MQyJM30+nOrxonMwgE07j2BPc481Pz3eNAvDkDg76IcvaEAVQHO3F3//q71x58SblSqq3U78/J73AQBUIXDz8Hz4rZ+71Hrgb+Sx33p6Hx69M/xQYI83iO2NzVhVV4NvPb0vpm2J5lOnYWlqzkGerPZ+P7oGA5jnSfvNMKfVlBfivEoX/vP1k/jcFQsgUr0mMFEKcA4yTVTIkHh2X7v11vKOr1yZcNEFiei5xyP3CekGOvr9oy81bciEywwDQGB4zqddU6HyXdC8oygCUkqs3vRq1PbRamErikBVcbhsbWv3EMTw/iOPL3Paor4WAOa4C6PmJdtUZdT59F2Dgag5+GYljJBh4FT3EILDD70qIn4bsqmeN397Rmg80Q0AWFxVlOGW5J7rls/EkY5B7DzYkemmEE1KpuovU+4aGTO6IePH0HBFgch6yiP3kQA+teEVXP7wCwjqCc4zvNT0pza8gnfP9Fv7XFxThr//8BJ86+l9uHL9Tnxqwys40Naf87VoKdbImtnA+Jd1jpwzP/L4Hm8w7vlsEbW3E8WuuX3kOcw5+Wf7/bhl82u4cv1O3Lr5NZwdCOD65VUx58mmpamZII/wxvEu2DWFFSwm4bJFHpS77PjJS0cz3RSiSSmwKXGX9y2wsauk+MyayWbMbGs4GbPk7qbhucDFThUb19bFXVhk09o6fPuZc9MuNr94JGEsRi4zbJ5n3VWLrPq0AJegzmfxlkcf77LOHpcd8zyFWL86Ov42rlmJ7Y3Ncc9XVeTApuGY3rTzSMyxZk3vTcOxPXL7P3xkmbXENRCOzfue3B21VHU2Lk3NOcgj3PjIyzCM8LKNNHG/e7MV//lGM35z32W4eK47082Jh3PlKKETnYP40Y7DuPuDC63KAVteOoovXHMe5qX/j2bGao4wayabNbsrCu3o9AYR0g1oqoKqIgc0TUFr9xBeOdSByxZXQhGAlLCO0RSB9/7Ljqjz3lJXjS9duxhB3bBi8YvXnIcPrt9p7WNWxVg6sxhXRmw3vfLQ1ayDnIemUgHCMCR6vAF4A7pVL9msYqHL8EBBhcsRdb5QyAjHuG7AoSlo6/ejyKHBpiqQkDjZOYSlM4thAPAHDSgiPLXDMCTa+/341IZdMe34zX2XobLYAQGks4oF5yBPxoA/hH2n+nDTRXMy3ZScdd3ymfj922fwL79/F7/4/Ps4F5lyiqYI7DraiV82tljbqt1O/O2HFmewVZTtRtZMNgwJW0CHlBI2VbFu+nZNxXPvtqPU5bBKwG1vbMa3P1UL4FwpONOuo524dvmMqAenvnzd+TFl5LY3NuObn7gwbjmvbHrLmpInsmb2eMRLqMtd478/a5qC2WVO6yHBQMjAod6BqIcEf/fFyyENQBHnEt7OwQB6hoJxY7NnKIgZJQXZtpCIhQlyhDdP9sCQwJIZxZluSs5y2lWsWjkHP33lOJ7d14YPXzAz000iGjePM/4S1yzzRuM1WvmqyLJsI5ePVhQRU45r49o6PPL8QQDnpmFUFMY/R2Wccl7Z9pY1ZUaySqolWur6sV3H8A8fWYa2Pn/MNRZXFmGepxDfvWVFVFm59atr4SmyQzeMhHWSM41TLCL8y//sx09ePobNt9ehMM0reOWTkGHga//1Nob8Op69/4NwZ1cHzbcCKSGzZNfI0bl//PgFLPNG4zJa+SoAo5a2GrnU9GOvHMXK+Z6YkeLxLEGd5oUXGKtZLFkl1UZb6lpTBf5qQ+yS1mZcmlM6QkZ4ASZDSvxox2HsOtqZjtJukXJrioUQwgbg11LKjwshCgBsA1ADoAnAHTJNWfxz+9qwfFYJk+Mp0hQF9165CF/7r7fxwLa9+PHt9VCz8K9DopFGluwy/Z+P8ZkEGpthSHiDsaXXIstXjfbayKWmf/zyceDl41H7/5+PGWMuQU0UKdES1RMtqTbaUtfBUPyyhGZclrscaA0MxZ0nn02l3SJl/NFsIYQTQCOA64Y3rQXQIqVcAcAdsT2ljp0dxJGOQaycW5aOy+W9eR4X1rx3Hp7b345vPb0Puf5OBU0PLPNGk2W+/XykfTBhCa6JlOdKtG9kya2xzkEETK0sXCSbFj/2bJoyrmskqx3pkvEEWUrplVLWAjCfirkGwJ+GP98B4Op0tOP5/W0AgJXZWXkhJ91w4Ux89MKZ+Nmu4/jqr99CcLiAPVG2KipQYkp0bVxbh6KCjHeVlOU6BwO4+/EG/OD5QzEl3My5wBMpz5Vo36oix6RLfNH0NJWycJE0RcSUeFu/uhaaIsZ1jWS1I12yZg6yEOKwlPI8IcQfAayXUj4nhPhrAJdIKT8/Yt97ANwDAHPnzq07ceLElK//qR+9gs7BAB5eVTvlc9E5Ukpsa2zBr/e0YkV1Kb5368VYUJHRGtNpHQpMRaxS6rR2D+HXjS24aWU1pJQQQuC3u1vwV3XVeT8HmbE6Na3dQ7j84RcAnCu9Vua0odrtxKxSpzUXeCLluRLtO5USXynCWM1yyYiZ1u4hfPGpPVZs93iD2LTzCH5428WY4y4c1zWyIHZzaw7yCGcBlA5/Xjr8dRQp5WYAm4HwBP2pXnDfqT7sae7B7e+bN9VT0QhCCNxcX4Oa8kL85M9H8eHvvoR7r1qEdVcugtOenW+rJFOyY5VSy66p+EVjC77z3CFrW7XbiVvfm/99A2N1asy3j1u6vdjT3IPPP9FoPaQUmQBMZJ5won2n+1xjxurEJSNm7JqKjgG/VXYQiJ4iMZ5r5FLsZuP7hs8DuH7482sAvJDqCz71+gnYVIEPLq5M9aWmrfct9GD96hWom+/G958/hKv+7QVsb2zhMqiUVXLtLUDKHowdynfTLcazcQT5SQB/JYRoArAX4YQ5ZToH/PjNnla8b4EHRQXZ+OPIH+5CO/7mmsW4ftkMbP3LCXzlV3vx6K5j+MePX4BL5pdnunlEUBSBJTOK8Zv7Ls+mt68pBzB2KN9NtxjPmoxQSnne8L9+ADem67rff/4QvAGdq+el0dJZJfi/N12IVw6fxc/faMbNm17FTRfNxlc/sgwzSwsy3Tya5nLpLUDKLowdynfTKcazJkHOhH2n+vDkaydxzdIZmOPOzqUO85UiBD6wuBKXzC/H7/aewtNNp/DsvjZ88erz8LkrFqDAlv/zk4mIiCg7ZeMc5LToGQrgnicaUFpow8111ZluzrRVYFNxS30N1q9egQtnl2D9Hw/gmn/bie2NLQixLBwRERFlwLRMkE/1ePG/tryGM70+fPnaxShx2jLdpGlvRkkB/u66Jfjax5bBYVPxlV/txYf+/UU88doJDPhDmW4eERERTSPTaopFe78Pv2powaYXj0A3JP7++iVYPKM4082iCBfMLsU/ffJCNB7vxm/3tuLr//U2/vmZ/bh2WRWuWVqFSxeUY06ZE0Lk50MBRERElHl5mSC/fqwLTS098AV19AwFcbrPh4Nn+nG4fQASwMq5ZVj73nmYVcZ5x9lIEQKXLChH/Xw3DrcP4MWDHXj50Fk83XQaAFDqtGFhhQuz3U5UuOwocdrgtKtwaCpsqoAiBK48vxI15Wlf2IGIiIjyQNaspDdZQogOALm+jE4F4iyIkoNy4fs4K6W8IRMXTnGsZvPPnm2buAoA7+ZYrGbrzzKeXGorkP3tzdd+daKy/f9pvPL5+xh3rOZ8gpwPhBANUsr6TLdjqvLl+8hF2fyzZ9smLlvbNZpcanMutRXIvfZOV/ny/8TvI2xaPqRHRERERJQIE2QiIiIioghMkLPD5kw3IEny5fvIRdn8s2fbJi5b2zWaXGpzLrUVyL32Tlf58v/E7wOcg0xEREREFIUjyEREREREEZggExERERFFYIJMRERERBSBCTIRERERUQQmyEREREREEZggExERERFFYIJMRERERBSBCTIRERERUQQmyEREREREEZggExERERFFYIJMRERERBSBCTIRERG4ysu7AAAgAElEQVQRUQQmyEREREREEXI+Qb7hhhskAH7wY7wfGcNY5ccEPzKGscqPCX5kDGOVHxP8GLecT5DPnj2b6SYQjQtjlXIFY5VyBWOVUiXnE2QiIiIiomRigkxEREREFIEJMhERERFRBCbIREREREQRmCATUULNXUP486GzaO/zZbopREREaaNlugEjCSFcAJ4CUAHgFSnlg5loh2FIdA4GEAjpKCpQMOAzoAjAkIBuSLgcKnxBAyFDQlMECmwK/EEDDpuCoC4RMiR0Q8KmCDjtCrxBA3L4WE0RKLAr8AYM6IaEqggoCqAqCipcDiiKiGmDEAKqABRFgcdlt/YhShXDkLhty2to7vai0K7i1a9ei1KnLdPNIkqrYFBH+4Df6usdmgIJEdUP+3wh9PmDMIBzfb+qAJCQEvA47bDbVXQOBqAqEt6AASEAKQFDSqiKgqoiBwCgfcCPoG7ApiooL7Ch0xuwru1x2lFQoEW1qUBTYAAIhAwUjbgvVbrssNuz7jZPUzBaXgAAZwf8UBUgpEsEh2PRrinQFAF/yLBiU1OAoC6hSwlVCNhUEf56OCdxaAoGAzqcmoKQBEK6AVUJ7yclrHPbFAEIABAx8VpgV6AbQLnTjj5/EL6AjuDw+W3Dr/tDEsGQAbumZl1uk42/OWsAvCal/BchxDNCiGVSyv3pbIBhSBxo68fdjzfg03XVuGrZDDzy/EF85rIFeGh7Ey5b6MHa98/DfU/uRku3F9VuJzaurcPxjj4smVWKzoEAHtjWZL22aW0dhAA+/0Rj1P6PPH8Qz+5rR7XbiYdX1eKxXcdw/3VLsGRGMQBYbTCPGblPNgUS5Z+XDnWguduLDy2rwnP72/FM02nc9t65mW4WUdoEgzrebR/AvVvP9d0b1qzEM3tb8cmVNVgyoxiBgI6WPi8G/SEMBfSovv97n74Im186ggduWIpgyMDrR8+ibkFF1P0k8j5RYFNw56NvoKXbi0durcX8ypKoa29cW4fFHhcOdQ7i3q2NqCxy4MEbluCBbYnvS0sqXUyS80RkbjIyL/iHjyyDP6jjL0fP4vLzq3C23x8VixvWrMQPdxzCs/vacf3yKnzpmsW4NyJWIl83v37x3XbULyiPOs/GNSthSIkvPLXH2vadm1dgx/4zuPGi6ph4bTx2FlecX4VebxB/+/M3rde+e8sKlLnsuGs43qvdTmy5oz6rcptsnGLhB1AohBAACgAE0t2AzsGAFYA3rQz/h6+qq7E6s7s/uNDqhACgpduLe7c24uJ5HrR2+6xgMl9bt7UR7X3+mP1X1dVYXz+0vQmr6mpw9+MN6BwMRLUh0T5EqfTUX06ixKnhjvfPxxy3E9sbWzLdJKK0ah/wWzd8INwP3/fkbqyun3uur/YG0NzlRddgMKbv//Iv3sSquhq0dHlxzxONuGb5rJj7ibnvuq2NaO7yWtsunueJufa9WxvR6Q1Y29ddtci6ZqL7UgfvFXljtLzgROcQ7h6OsZYub0ws3vfkbivnWFVXYyXH8V43v75pZXXMee59cje6BoNR277yq71YXT83brxes3wWmru8VnJsvnb/L/eiJSLeW7q9WZfbZGOC/BSAjwDYD+BdKeWRkTsIIe4RQjQIIRo6OjqS3oBASLf+0wwp0dLtRZnTZm1TFWF9bmrp9kI3JArtatzXCu1qzLayiLerI68RCOlRbUi0D2W/VMdqqvQOBfH8/nZcubgSNlXBBxdXovFkN46fHcx00yhFcjVWUylkyLj9sHkPCIR0hIb7/UR9f5nTZr0W734SuW/kfUJPcO3INo3nvhQyJrR4WE6YrrE6Wl4QGWOjxSKAhPE3Micx43XkfvHymUTxJ0dpT7zzZFNuk40J8lcBbJJSLgVQLoS4bOQOUsrNUsp6KWV9ZWVl0htg11RUu50AAEUIVLud6PEGrW26Ia3PTdVuJ1RFYCigx31tKKDHbOvxBmO+rnY7YdfUqDYk2oeyX6pjNVX2tvRAlxK11WUAgEvnlwMAXj3amclmUQrlaqymkqaIuP2weQ+wayq04X4/Ud/f4w1ar8W7n0TuG3mfUBNcO7JN47kvaVnydnUyTddYHS0viIyx0WIRQML4G5mTmPE6cr94+Uyi+BOjtCfeebIpt8nGBLkYgPnIvB9AUbob4HHZseWOelS7nfjt7hZsXFuH7Y3NeHhVbXiezEtHsWHNSus/3Jxrs+dEJ+a4C7B+dW3Ua5vW1qGqxBGz//bGZuvrh1fVYntjM7bcUQ+Pyx7VhkT7EKXKW629AIAFFS4AwIwSB1wO1dpONB1UFTmwcW1dVD+8Yc1KbGs4ea6vdtpRU+5EucsW0/d/79MXYXtjM6rLndh8ex127Dsdcz8x9920tg415U5r254TnTHX3ri2Dh6n3dq+aecR65qJ7kuVvFfkjdHygnmeQmwZjrHqcmdMLG5Ys9LKObY3NmPjiFiJfN38+re7W2LOs3HNSpS7bFHbvnPzCmxrOBk3XnfsO42acie+f+tFUa9995YVqI6Id3MOcjblNkLK7Hr7RQgxH8CTCCfvJwHcJqVMOOZeX18vGxoakt6OZFSxMIZfG18VCwFVEaxikXoZ+8GlKlZT4Z7HG/BWay/+/ZaLrG3/9Mw+KELgv790RQZbNq0wVrPAZKpYGIaENoUqFiHdgDbBKhbBkBFzX0pjFQvGappMtIqFYUjYIqpYmLFpVrEwpIQynioWhmFVuzCrWJg5zkSqWIQMCSWzVSzGfYGse7RVSnkcwOWZboeiCFQWO6yvS52j7DwO7ngbXRNrA1G6NLX0YmFldIAuqHDhj++csUpQEU0HNpuKOe7CUfcpKNBQUDD27dTqz0fp+2eXRd9s5sQ573jaRPlprLygqqQgadeqmMQx8eIVAMo1x5g5T7bhXY6IorT3+3Cmz4dFldGzmxZUuBDUJQ629WeoZUREROnBBJmIorzVEp5nvLAidgQZAN7mPGQiIspzTJCJKMrh9gEAQHV59Fu4M0oK4LSpeOdUXyaaRURElDZMkIkoyvHOQRQXaChyRM8lU4TArNICHO8cylDLiIiI0oMJMhFFOX52CDMTPOgxo6QAxzoG0twiIiKi9GKCTERRjp0dTJggzywtQGuPF4GQkeZWERERpQ8TZCKyeAM6zvT5MLM08QiyIYHWHm/c14mIiPIBE2QispzsCs8vnpFoBHl4+/Gzg2lrExERUboxQSYiy7HhxDfRCLK5/XgnE2QiIspfTJCJyGImvrMSJMglBRqcNhUnWMmCiIjyGBNkIrKc6BxESYGGQnv85UKFEJhZWsARZCIiymtMkInI0tLtRVWxY9R9qood1lQMIiKifMQEmYgsrT1elBeNniBXFjtwuscHw5BpahUREVF6MUEmIgCAlBKne3yocNlH3a+iyIGAbqBzMJCmlhEREaUXE2QiAgD0DAXhDerwjDGC7CkKJ9CnWAuZiIjyFBNkIgJwbvGPijESZPP1071MkImIKD8xQSYiAOdGhM0R4kQqXOEEubXHl/I2ERERZQITZCICAJzuDSe8njHmILscKgpsCqdYEBFR3sq6BFkIcZUQ4s/DH81CiM9kuk1E08GpHi9sqkCJ0zbqfkIIeFwOJshERJS34q8GkEFSyp0ArgAAIcQzAPZktEFE00RrjxeeIgcUIcbc1+OyW3OWiYiI8k3WjSCbhBCFAM6TUjZlui1E08GpHu+Y0ytMniKOIBMRUf7K2gQZwHUAns90I4imi1M9vnEnyBVFdpwdCMAX1FPcKiIiovTL5gT54wCejveCEOIeIUSDEKKho6Mjzc0iGr9ciVXdkGjv96HcNXqJN5PHKvXGShb5IldilYixSumQlQmyEEIAuBrAjnivSyk3SynrpZT1lZWV6W0c0QTkSqx2DvhhSKDcNfoDeqby4ZHmM0yQ80auxCoRY5XSISsTZACXAHhHSsm7L1EanOkL/6q5C8c3xaJ8eL+2Pv6KEhFR/sm6KhYAIKV8HcAnMt0Ooumirc8PAHCPcw6ye3ik+QwTZCIiykPZOoJMRGk00RHkQrsGp03hCDIREeUlJshEhPY+HxQBlI6xSEgkt8vOBJmIiPISE2QiQlufD2WFdqjK2IuEmNyFdj6kR0REeYkJMhHhTJ8fZYXjHz0Gwg/qMUEmIqJ8xASZiNDW67MqU4yX22VHe78fhiFT1CoiIqLMYIJMRGjrD0+xmIhylx0hQ6JzMJCiVhEREWUGE2Siac4X1NEzFIR7ElMsANZCJiKi/MMEmWia6+gP10AuH2cNZJObq+kREVGeYoJMNM2ZI8CTmWIBcLEQIiLKP0yQiaY5axW9CU6xKHXaoIhwDWUiIqJ8wgSZaJpr75/YKnomVREoddqsBJuIiChfMEEmmuba+/1QFYGiAm3Cx7oL7Wjr5wgyERHlFybIRNNce58fZU4bFDH+VfRMZYV2tPEhPSIiyjNMkImmufZ+34RX0TO5C21o6+cUCyIiyi9MkImmufY+/4QrWJjcLju6BgMIhIwkt4qIiChzmCATTXPt/T6UOSc7ghxOrDsGOIpMRET5gwky0TQWCBnoHgpOfgR5eGoGFwshIqJ8wgSZaBo7OzC5GsgmczU91kImIqJ8wgSZaBprH37ArnSSCXL58MhzGxNkIiLKIxMvfJoGQogHAXwcwACAm6SUgQw3aVwCgRA6BgMIGRKaIlBgU+ALGlAUQEqBoG5AVQRsioAQgCEBw5DQpYQiBBQBSAkoioA/ZMCmCLgcCnxBCSHCx+tSokBTEdINhKSEy6FCN4BgyIBdU+EZHtHrHAwgENInvE1RRi/1ZRhywsdQ9jJHfie6SIipqECDpghWsqCMMAyJPl8AQ34dsPpPQIFA0JDQDQm7qsCmCkgIFNtUdHrP9dEepx0FBRp8vhC6fUEEIvroUoctal+bKhDUJYqdCorsDvZ7NCmR91CbpkBVwlPdzJg1pIQqwjlC+F8BQ0qEhuPZpiootAt4AxJB3YBtOL59IeNcDjGcX+jD5zJjVx+OZYdNQUiX4esZEpqqAJAABKqKHNA0jp0CWZggCyEWArhASvkBIcTfAKgGcDTDzRpTIBDCgY5B3Lu1ES3dXly/vApfuvZ8PPL8QXzuioX4yq/2oqXbi2q3E+tX12JWWQE6BwL425+/aW1/eFUtHtt1DHddvgD/+ocD6BjwY9PaOpQVamju8uKBbU2oLHLgwRuWxHxunmPLHfVwaAru+Onro257/LOXwh8ycPfjDVH7LZlRnLDjNwyJA239EzqGsps5gjzZh/QUIVBWaOMIMqWdYUi09gyhZyiIR3YcwmcuW4DHdh3DfVefB29Aj+oXN62tQ1WJDYc6fVYfXe12YuPaOiz2uHCkaxCff6Ixqo+uKHbg3VM9+NLPm1DtdmLDmpV4Zm8rbryoGjNKJCpcBez3aELi3UN/dNvFUITAgD8UFbPfuXkFygo1+IIGhuLE8w+eP4hn97Xj+uVV+OI1i/HDiN+Bz1y2AA9tP7f/hjUr8cMdh/DsvnZUu5346Z316B0K4v5f7o263n/8+Sj+5trzsXRGMZNkZOcUi2sBuIUQLwH4AIBjGW7PuHQMBqyOFwBW1dXg3q2NWFVXYyXHANDSHU50Qzqs5Njc/tD2Jqyqq8ED25qw7qpFaOn2Yt3WRgDC+uVYd9WiuJ+b57j78Qac6Bwac9uJziHrlzRyv87BxIP1nYOBCR9D2W2qUyyA4dX0mCBTmnUOBuAPSdz75G6sqqux+s/uwWBMv7huayOCIUT10S3dXty7tRGd3oCVHJvbH9jWhJYuLy6e57G23ffkbqyun4t7tzYiEJLs92jC4t1DuwaDODsQiInZr/xqL1RFRVeCeF5VVwMgnGvcN+J3wEyOzf3N182vW7t9VnIceb1VdTVYt7UR7axKBCA7E+RKAB1Syg8iPHp8xcgdhBD3CCEahBANHR0daW9gPCFDWsEGhEfkWrq91r+RWrq9UATibo88ztymR5w78nyJzl1oV8fcVmhX4x4bCOkJv8dASJ/wMdNdNsZqpI5+H0qdNmjK5LsCd6GdVSzyQLbH6kiBkG71o5H9ZqK+bWQfPdb2QrsK3ZBR21RFWH0y+73MybVYNcW7hxba1YQxq4jE92ozRxiZayTKCyLfJRztnC3dXoR01rUHsjNB7gNwYPjzowDmjNxBSrlZSlkvpayvrKxMa+MS0RSBarfT+rrHG0S122n9G6na7YQhEXd75HHmNjXi3JHnS3TuoYA+5rahgB73WLsWnUhHsmvqhI+Z7rIxViOFFwmZ/OgxEK5k0dbHEYdcl+2xOpJdU61+NLLfTNS3jeyjx9o+FNChRkyhqHY7oRvS6pPZ72VOrsWqKd49dCigJ4xZQya+V5s5wshcI1FeYO5vXnO0/CM8J5my8afQCOCS4c/PQw7MPwaASpcdG9fWWUG3vbEZG9fWYXtjM75z8wpruzm/TVOB7996UdT2h1fVYntjM9avrsWmnUesuUaAxPrVteGvdx6J+7l5ji131GOep3DMbfM8hdhyR33MfubDe/F4XPYJH0PZrW14BHkq3IU2DPhDGPSHktQqorF5XHY4NIGNa1Zie2Oz1X+6XbaYfnHT2jrYNET10eYcZI/Tjh/fXhfTR1eXO7HnRKe1bcOaldjWcBIb19bBrgn2ezRh8e6h5S4bKorsMTH7nZtXQDd0lCeI5+2NzQDCucaGEb8DD6+K3t983fx6jrsA371lRcz1tjc2h+frFznS+nPJVkJKOfZeaSaE2AigHsB+KeUdo+1bX18vGxoa0tOwMYxVxSKkG1DGWcUiEDKgjVnFAnA5FFaxmJiMNTabYtV06befw7JZJVh35aJJn+PlQx3YsPMIdnzlSiysLEpi66Y9xuoYxqpiYQw/9T/eKhbBiD6aVSwmhLE6TvGrWEgoAIKGhIzIB5QRVSzMihOJqliow3lFTBULTSAYSlDFQkpoigIBCTk9qliMO1azrooFAEgp7810GybDbtcwx578H2npJI6pLI79C3C820ajKGLCx1B20g2JzoHA1KdYWLWQ/UyQKa0URaCs0IGywvEfM6cgto8uKNAwK872ePsSTUWy7qETiXmanLz+M4GIEuseCkCXEmXOqb1VbK6mx0oWRESUL1KaIAshZifYPjeV1yWisbX3TW2ZaZO5mt4ZJshERJQnUj2C/FSC7f+c4usS0Rja+8MJbdkkV9EzOe0qnDaFI8hERJQ3Uj3BShdCfGPENgFgYYqvS0RjsFbRm+IIMmCWemOCTERE+SEdTyA8jdinBi9Ow3WJaBQd/eYUi6mXqypzshYyERHlj1QnyM9KKXcLIWYCGJBSDgCAEOJPKb4uEY2hvc8Hl12FPQklfcpddhw9O5CEVhEREWVequcgv2/4348CWCaE+C8hxH8CuEsIsTzF1yaiUbT3+6c8/9hU7rKjvc8Pw8i+uupEREQTleoR5BIhxH0IJ8rnAzBHjv1Syn0pvjYRjaK93z/lVfRM7kI7QsMF8Fknm4iIcl3KRpCFEP8OYBaA/QBaEV4y+i0Ab6fqmkQ0fm19PquG8VR5isLnOd3rTcr5skE2rjJKRETpkcoR5C0AhgB8E8BWAHsAfAOAH8CMFF6XiMYgpUR7nx8X1ZQl5Xzm0uWne32orU7KKTOmdyiIu372Bt5u7cXl53mw+Y562FSuqURENJ2kLEGWUu4H8DUhRBHCqyX3Syk/AQBCiMuFEAullEdTdX0iSqzXG0RAN5JSwQIAPEXhaRWne3J/BPkbv3sbe1t6cPkiD1440IFvP7Mf3/zEBZluFhERpVEqp1ioQoinpZQDUspWKWVfxMuLALyeqmsT0eja+pJX4g0ASgo0aIrA6d7croX80sEO/PbNU/iri+fg3qvOw4cvmImf7TqOw+2s0EFENJ2kLEGWUuoApBDiQSHEfCHEHiHEC0KIXQBuBXBLqq5NRKMzF/Vwu5LzkJ4QAp4ie84nyD9/4yRKCjR8YsVsAMAnL5oNVRH4xRsnM9wyIiJKp3RMrAsBCAI4LqW8Wkp5mZTyo1LKHWm4NhHFYSXISRpBBsKl3k7l8EN63YMB/GlfGy47rwLa8JzjskI76ua6sa2xBYGQkeEWEhFRuqTzyRM+Ek6UJdqTuIqeyeNy4FQOz0H+76ZTCOoSV55fGbX96qVV6B4KYueB9gy1jIiI0o2PZhNNQ219PhQ5tKSsomfyFIUXC9FzdLGQP75zBnPcTsz3uKK2XzinBE6bihcPdmSoZURElG6pfEhvHcKjxmL4g4iyRFufD+7C5Mw/NpW7hhcLGfAn9bzp4AvqeONYN1bMKY15TVMUXDC7BC8e7GBtZCKiaSIlCbIQQgPwHgAXAugCMAgmyURZo60vectMmzyucKm3Uzn4oN7rx7oQ0A28pzo2QQaA91SXoqXbixOdQ2luGRERZUJKEmQpZUhK+QUAtwH4IoCFAA6O51ghxA1CiBYhxJ+HP5akoo1E01kqRpArhlfTy8V5yH8+fBaaIrB0Zknc12vnhBdUefkQp1kQEU0HqVxJD1LK14QQqwCEpJQPTeDQjVLKb6eqXdnEMCQ6BwMIhHTYNRUelx2KMvpg+2SOSWf7KLsZhkRHvx+XLihP6nkri8MjyM1duTfK+vKhDiyZWYwCmxr39RklDlQWO7DrSCduf//89DaOAET3RUIIqAJQFGXMPol9GOWayJh12lWEDIlgyJhw/DL2pyalCTIASCmPT+KwVUKImwA0A1gt83Tin2FIHGjrx92PN6Cl24tqtxNb7qjHkhnFCYN4Mseks32U/bqGAggZEmXO5E6xKLRrKHJoaO7OrQS5zxfEu6f7saou8RrZQggsrirC7pPdaWwZmeL1RQ+vqsVju47h/uuWJOyT2IdRromM2coiBx68YQke2NY04fhl7E9dNlaxOALg61LKSwHMAnBlhtuTMp2DASt4AaCl24u7H29A52Agqceks32U/c4MzxH2FCU3QQbCo8jNXVOfYtHe58Onf/wqrlz/Av7xt2+ntDLGmyd7IAGcP6N41P0WVxWjrc+fk1NIcl28vuih7U1YVVczap/EPoxyTWTMrrtqkZUcAxOLX8b+1GVjgtwF4Lnhz48DqBq5gxDiHiFEgxCioaMjd+cEBkK6Fbymlm4vAiE9qceks30ULRtj1Uzwyl0pSpCnOILcPRjAqk27sLelBx6XHY+9egL/73/2J6mFsRpPdEMRwKJK16j7LZ5RBAB5O4qcjbFqStQXlTlto/ZJ7MPyUzbH6lRFxqwZ35HGG7+M/anLxgT57wDcKoRQEK6C8fbIHaSUm6WU9VLK+srKypgT5Aq7pqLa7YzaVu12wq7Fnwc52WPS2T6Klo2xemZ4FT1PChLkqmIHWru9UyqH9qMXDqO124uvfmQZHvjwUnz4gpnY8vIxvH6sK4ktPafxRDdq3IUotI8+42yepxB2VcHuEz0paUemZWOsmhL1RT3e4Kh9Evuw/JTNsTpVkTFrxnek8cYvY3/qsjFB/iGAuwD8BcBvpJT7MtyelPG47NhyR70VxOYcodESl8kck872UfY73euDpgiUOJNbxQIAKosc8IcMdPRPrhZya48Xj796Ah9YXGlNebjt0rkoKdCw6cUjyWwqAEA3JN5s7rFGh0ejKQoWVLrydgQ5m8Xrix5eVYvtjc2j9knswyjXRMbspp1HsH517aTil7E/dSl/SG+ipJSnAVyV6Xakg6IILJlRjN/cd/m4nzKdzDHpbB9lvzO9PpS77FBE8v8frUoW3UOoKimY8PE//fMxGFJidcQDc3ZNwfUXzMS2xhYcbOsfc67wRBxuH8CAP4TFVeM753mVRfjTvjYEdQM2NRvHF/LTyL7IrGLx7U/VjtonsQ+jXDMyZp12Fb++77IJV7Fg7E9d1iXI042iCCupSOUxk5XOa1F6nO71wp2iUYSq4nBS3NzlRd28iR0bCBn49e4WrJznRkVRdMxdv3wGfvtmK37+ejO+8fHlyWou9raEp0ssqhp7BBkAFlS4ENANHG4fwLJZ8WsmU2pMti9iH0a5Jlkxy9ifGg6BEE0zp4dHkFNhKrWQdx5oR/dQEFeeHzunsLjAhhXVZfj9W6dhJLGiRVNLD5w2FbNKxzfavaAi/CDfW629SWsDERFlHybIRNOIlBJnen0pm4dm1xS4C204MYkEeVtjC8qc4UQ4nvcu9OBMnw97mpM3B7ippRcLKlzjnm4ys7QATpuCd5ggExHlNSbIRNNI91AQ/pCRshFkAJhV6sTRjoEJHTPoD2HngQ68b6EHaoI5civnlsGmCjzTdCYZzUQgZGD/6T4sHKO8WyRFCMzzuPB2a19S2kBERNmJCTLRNHK6N1wX0+NK3by02WUFONwxMKFSby8d7EBAN3DJfHfCfQrtGt4zpxR/2pecBPnAmX4EdYmFFeObf2yaX+HCvtN9KV28hIiIMosJMtE0Yq6il8oR5NllTvR5QxNasemP75xBcYGGJTNHf/CttroMzd1eHD87ONVmoql1+AG9CYwgA8ACjwveoD7hUXIiIsodTJCJppFUrqJnml0arrt5pH18CWRQN/D8u+1YOdedcHqFqba6FADw0qGpr571Vksvih3ahJ/ynucpBADsO81pFkRE+YoJMtE00tLthU0VKCtM/iIhptllwwlyx/hGed843oV+Xwh1cxNPrzDNLClAVbEDLx2ceoLc1NKL+RUuiAnWg55T5oSmCOw/3T/lNhARUXZigkw0jbT0eFFR5EjJIiEmT5Eddk0Z9xSEHfvboSkC7xkeHR6NEAK11aV45UgnAiFj0m30BXUcbOu3yrZNhKYqqHY7OYJMRJTHmCATTSMtXUMxi3AkmyIEZpUW4Mh4E+R327FsVgkKbOq49r9wTim8AR1Nw4t8TMaBM/0IGXJCFSwizS0vxP5TTJCJiPIVE2SiaaS1x5uWlZVmlzlxsG3sKQgnOgdx9OwgVs6NX0lxpAwAACAASURBVPs4HnMFu1ePdE66fU3DdYwXTmIEGQDmeVzoGPCjo98/6TYQEVH2YoJMNE34gjrODgRSPoIMAPM9LrT2+NA1RiWL5/e3AwAuHsf8Y1NJgQ3zPIV47ejkE+S3W3pRXKBN+mdhPqi3n9MsiIjyEhNkommipTtcwSIdI8hm6bSxpkE8t78N1W4nZpSMb6ln07JZJWg40Q1/SJ9U+5pae7DAM/EH9EzzysPfHxNkIqL8xASZaJpoHS7xVpmGEWTz4bemlsRLMvd6g3j9WBdWTmD02HTBrBL4QwbePDnxecjegI6DZwawqGpiC4REKirQ4HHZmSATEeUpJshE00RL9xAAoKIodTWQTYV2DbPLCkZNkF882IGQIVE3b+IJ8tJZJRAAXjvaNeFj3znVC11KLKqcfIIMhB/UYyULIqL8xASZaJpo6fZCUwTchalPkAFgYUUR9o4yxeLZd86gxKnhvEkkqkUODfMrXJOah/xm8+RW0BtpnseFI+2D8AUnN82DiIiyFxNkommipdsLT5Edyhir1SXLwkoXOvr91up9kQb9ITy3rw2XzvdMuj3LZhZj98nuCSeoe1t6UVFkR9kU/1CY5ymELiUOj3PFQCIiyh1MkImmieNnBzGjeGIPw03FBbPDC3/sPBC76t2f9rXBFzJw+XmeSZ9/+ezS8Dzk5onNQ37zZDcWTnF6BQDMK+eS00RE+YoJMtE0IKXE8c5BzCxNX4Jc43aiqtiBP+07E/Pa795sRUWRHefPKJ70+ZfOLIbAxOohdw740dztnfL8YwCYUVIAh6ZgHxcMISLKO1qmG5CIEOJ+AB+TUn4o021JBsOQ6BwMIBDSUWBXEAhKBA0DqhAQAlCFgC6BkG5AVcLbHKoCX8iAbkhrGyCgCkCXElLCes2hKTCkRFCXCBkSmiLgcijwBiQCw+e0KQKqImL2K3Yq6PcaEAKQElAUwDAAQ0ooQsCmCoR0ieDw/poi4A8Nn1MVCOoSTruKEocN3d4gAiEdQoTbqSgK3M7o7UJIGAbgtKsoc4bf5jZ/NnZNhceVvmkA00XXYAD9vlBaE2QhBOrmufH8/nYM+kNwOcLdzYnOQew82IEba2dPaclrl0PDggoXXj3SifuvG98xDSe6AQBLppCYmxRFoIYP6k1JMKijfcAPmxruS4YChtUv2VQBIQQCofA2myJg1xQMBnRoioDTrqDfp0NVBOyqAiklfCEDmiJQ5FBQaLPBNs7VGYkSibx321QFDhsw5D93X3XZFQR1IKCH79U2RcBhUxDSJQwJa78CTYE/dO4+qw/fX7Xh+7KU0edw2hV4I34fVEXAN3zfddoUqIrAoF+37te6IaGpCqqKHNA0jn0mQ1YmyEKIeQDuBBD73mwOMgyJA239uPvxBlQWOfDgDUvwwLYmtHR7Ue124js3r0CBTcEXntpjbfvJZ+oQDEnc++Rua9vDq2rx2K5j+MLV58EXNPCVX+21Xnv0znr4QxLrtjZa2zaurcMjzx/Es/vaUe12Yv3qWswuK0C/T4/Z73hHH8qLnHhs1zF85rIFeGh7uH3XL6/Cl65ZHNWO9atr8a9/OICOAT82rlmJp/e24qqlM1BZ7MC//uFd63oPr6rFSwfa8PGLqqOuZ34fd12+ANVuJwb8Ou5+vMF6fcsd9Vgyo5hJchId7xwEgAnXG56q+nlu/M/bZ7Dj3XZ8fMVsAMDml45CEQIfvmDmlM9/4ZxS/P6t01EJ+GgaT3TDpopJLzE90nxPIf5yrAtSyknXVJ6ugkEd77YP4Ok3W/CZy+ejrT+EeyP6iUfvrIcvJKO2bVizEltfPYFdRzujPl+/uhaFdhXf/N2+cL+0tg4zSiTcsDNJpkmLvHe3dHvxzRuX4pKFFfj8E43W/fHBG5aio98fdU/ftLYOmgL89ePn9vviNYvxwx2Hou6v1W4nvnvLClSWOHC6xxdzjv9+swU/fvl4zH33R7ddDEUIPBLnfJvW1mHpjGImyUmQrT/B7wP4aqYbkSydgwHrF2zdVYusXwIg/ODUV361F12DwahtmqJaSam57aHtTVhVV4OuwaCVHJuvtXT7rCTU3Hbv1kasqquxvn5gWxOCOuLud/E8j3V+85cNAFbV1cS044FtTVh31aLwsU/uxur6uXhgWxOau7xR13toexNW18+NuZ55nQe2NcEfktbPxnz97scb0DnGCmw0McfPhku8zUpzgrxkZglmlRbgO88egD+k49jZQfyqoQVXnl+JctfUq2lcMLsEIUPi9ePjK/f2xrEuLKosgk1NTtc33+NCvy+E5q7YBxFpdO0Dfty7tRGr6+dCN4SVCAPn+rSR2+57cjfu/uDCmM8f2NaErsHguX5payMCIYn2AS4FTpMXee8GgGuWz7KSYyB8f2zu8sbc09dtbcTpXn/Ufvc9uTvm/trS7cX9v9yLkI6451hdP9f6OvK+2zUYxL0JzrduayPjPkmyLkEWQtwGYC+AfaPsc48QokEI0dDRkf2DzIGQbgVwmdNmfW5q6fai0B49yqEIxN2vzGlDoV2NeS3eNnP/yK8TnVc3pLV/5OuJ2muet6XbC1UR1vcw8nrma/GOH609gUmukJZtsiVWj3cOQhHpWUUvkqoI3HnZfBzvHML9v3gTt//kL3DYFNx00ZyknH/pzBLYVIFXDp0dc19vQMdbrb1Tmvc80vzhBVHeOZW43nOuSHeshob7HFUR0KUcd5+mDr+zNPLzyP7H7NNChkz590Hpl65Yjbx3A+FphyPvj4niNPKebt7vEt1PE90H1Yh3USPvu+Y1E50vpBuT/I4pUtYlyABuBHAtgJ8DqBNCfHHkDlLKzVLKeillfWVlZdobOFF2TUW12wkA6PEGrc9N1W4nhgLRCaEhEXe/Hm8QQwE95rV428z9I79OdF5VEdb+ka8naq953mq3E7ohre9h5PXM1+IdP1p77Fp+vC2aLbF67OwgKosd0JI0cjoRtdVl+MiFM/HcvnZ0ewN46IalSUvU7ZqC82cU4+VxJMhvNvcgZEgsnZm8BLnGXQhFAO/kwYN66Y5VbbjP0Q0JVYhx92n6cNI78vPI/sfs0zRO08pL6YrVyHs3ACgj4jTR/XjkPd283yW6nya6D+oRf+BF3nfNayY6Xyb6+XyUdT9FKeVtUsorANwKoFFK+cNMt2mqPC47ttxRH54ftPMI1q+utYLanINc7rJFbQsZOjauWRm17eFVtdje2Ixylw3fuXlF1GvV7gJsWlsXtW3j2jpsb2y2vl6/uhY2FXH323Oi0zr/w6vOtW97Y3NMO9avrsWmnUfCx65ZiW0NJ7F+dS1qyp1R13t4VS22NZyMuZ55nfWra+HQhPWzMV/fckc9PEl4+53OOd45mPb5x5HueP98/PTOS7BxTV1SKkhEWlFdhgNt/dZKgYn8+XAHVEVgSRITZLumoNpdmBcjyOlWVeTAxrV12NZwEqoisXFEP1HtLojZtmHNSmx56WjM5+tX16LcZTvXL62tg10TqErDsuqUvyLv3QCwY99p/Pj2uqj7Y025M+aevmltHWaVOqL227BmZcz91ZyDrKmIe45tDSetryPvu+UuGzYmON+mtXWM+yQRUmbnW1BCiPkAfjJWFYv6+nrZ0NCQljZNxbirWERss6pYSGltM6tYGDL8hOx4qlgEdQNKEqpYhIavpSkCfj3cTpsmEAxNtIpF+OeRoSoWGRtSylSsSilx4Tf/iMsXVeCuyxek/fqpdqonPI///950Ae54//yE+33sBy/DkBLfuPGCpF5/w87DePdMP974P0kvuJP3sTrVKhYDPh0Kq1hkg7yN1URVLMz76lhVLMz9zCoWigi/QzyZKhb+UPhcrGIxJeOO1aysYgEAUsrjAPKixBsQLgmV7vmfAFBWOL79kjW4mOh7HOt7z8TPZro43evDoF9HtXucwZBjZpc5MbusAH/a15YwQW7v9+GdU3249ZKapF9/QYULLx86i/Y+H6oyOEqfi2w2FXMi4tI9juIiFRGflyenGAlRQvHu3aXOBDsn2Vi/D+O9v9Pk8M8Mojx3oK0fAFBTnqZePQNWznXj1SOd6PMF477+4vBqfitqypJ+7YUV4Skje1s4zYKIKF8wQSbKcwfPhBPkfB1BBoD3LihHyJD4fdPpuK//8Z02lLvs1vLQyTS/IvygXlPLxJa8JiKi7MUEmSjPHWjrR7nLjqJxLKSRqxZVFqHG7cTP32iOea2934cX3m3H5Ys8KVnMw6GpqCkvxJvNTJCJiPIFE2SiPHewrR/VZfk7vQIIL2t91ZIqvNncg3fPRJdc+/XuVuhS4qolVSm7/sKKIjS19CJbH3omIqKJYYJMlMd0Q+Jw2wCqUzC1INtcsbgCdlXBIzsOW9v8IR1P/eUklswoxuwU/pGwqMqFXm8QJ7tGLzVHRES5gQkyUR5r7hqCL2TEFJPPRyUFNnx8xWw803QarxwOLxzyox2HcbJrCJ+8ODkr9yVi1nbec5LTLIiI8gETZKI89vbwAhapeDgtG31ixWzMKHHgnica8OWf78GGnUdwxXkVuCgF1Ssi1bgL4bSpaDjRldLrEBFRejBBJspje5t7YFMF5k6TBNmuKfjfH1mGhRVFeLrpNC4/rwKfuWx+yq+rKgKLZxSh4Xh3yq9FRESpl7+PtRMR9rb0Yr7HBU2dPn8LV5UU4H9/dJm1EmS6LJlRjG2NLej1BlHqtKXtukRElHzT565JNM2EdANvtfRa82Onm3QmxwCwZGYxJIDdJzmKTESU65ggE+Wpwx0D8AZ1LKzkerzpsKiyCKoi8MYxzkMmIsp1TJCJ8tTe4YUrzpumI8jpVmBTsajShV1HOjPdFCIimiImyER56vVj3Sh2aJhRWpDppkwb75lTiqaWHvQOBTPdFCIimgImyER5SEqJVw6fxfLZJWmfizudvWdOGQwJ7DpyNtNNISKiKWCCTJSHjp4dxJk+Hy6cU5rppkwri6pcKLSreOkQE2QiolzGBJkoD5kryb2HCXJaaYqC5bNK8OKBdkgpM90cIiKaJCbIRHnoz4fOoqrYgapiR6abMu2snOfGqV4f3jnVl+mmEBHRJDFBJsozQ4EQXj50FrXVpRCcf5x29fPcUATw+7dOZ7opREQ0SUyQifLM8/vb4Q3qeP9CT6abMi0VF9hwwexSPPPWaU6zICLKUVm31LQQQgPwnwBmAzggpfxshpuUFIYh0TkYQCCkw66p8LjsUBQx5j4AxjyOKNLTTafgLrRh6cySTDdl2nrvgnL85M/H8M6pPj4oOYpQyED7gB9B3YCmCDg0BRKC/RzlrNHu9WPlAePJEyh9si5BBvBJAHullDcLIf5HCHGRlPLNTDdqKgxD4kBbP+5+vAEt3V5Uu53Yckc9lswojvrFGbnP45+9FP6QMepxRJF6h4J44d0OXLO0ijGSQe9d6MHjr57AU6+fxD9/6j2Zbk5WCoUMvNvWj3VbG63+bcOalXhmbys+ubKG/RzlnNHu9QBGzQPGkydQemXjFIs/APj34ZHkMgA5/6RL52DACnoAaOn24u7HG9A5GBh1nxOdQ2MeRxTpP984iYBu4KollZluyrRW5NDw/kUe/NeeVgz4Q5luTlZqH/BbyTEQ7t/ue3I3VtfPZT9HOWm0e/1YecB48gRKr6xLkKWUA1LKIQCvAGiTUh4duY8Q4h4hRIMQoqGjoyP9jZygQEi3gt7U0u1FIKSPuk+hXR3zOMpu6YzVQMjAo68cw4VzSjDP40rptWhsH1pWhaGAjl/vbsl0U8Yl3f1qUDfi9m+qItjP0aiyNQcY7V4/Vh4wnjyB0ivrEmQhhEcI4QBwGQC3EOLqkftIKTdLKeullPWVldk/UmbXVFS7nVHbqt1O2DV11H2GAvqYx1F2S2es/np3C9r6/PjohbNSeh0an0WVRTh/RhF+9MJh+ILZf5NLd79qU5W4/ZtuyP+fvTuPj6q+F///+pzZMtlDSAIkCIgsogYlcYNba6W19kprvUTbCrhdFaS2t/6spb39+q3f0vYWqdfe1rJoWzewVaF2oa3aqrS3uBIoqNGIrAlLEkL2zH7O74/JDDOZCUlIZn8/H488yJw5mfmQnPM57/mcz+f9ln5OnFKyxgCnutYPFgcMJU4Q8ZV0ATJwD3CdYRg+oBewD7J/0ivOsfLojdXBgz8wtyiwCG+gfSYVZw/6c0KAf+7xAy/WM70sl9kTCxPdHAEopbi+eiJNnS42vnko0c1JOqW5NtYtrgrr39YsmsOm7YeknxMp6VTX+sHigKHECSK+VLKlIVJKlQNP4Q+M9wI39QXLUVVXVxvbt2+PV/NOm2SxSBoJ++XF8lhdsWk3z9U28P1rz2OyTK9IKj/40/scaO3hxa9dxoTCYX3eT8tjNVQgi4XXp2OSLBapLO2P1aGSLBZJb8i/0KTLYmEYxmHgikS3Y7RpmqJkkKpmA+0z2M+JzPb0m4d4ZnsD15w/QYLjJPTv/zKFb/5mN/du2sWTt16MSS54QWazNtwPDUIktVNd6weLA4YSJ4j4ScYpFkKIIfr1W4f4P799h9kTC7i+amKimyOiKMvPYvElk9j2USv3/e5dKR4ihBApIOlGkIUQg2vrcfODP73Pc7WNzJ5YwNfmT5dbcUls/swymjtdPP3mIXpdXn7wb+eRbZXuVwghkpX00EKkCK9PZ1djO3/YdZRntjfg8vi45vwJ1FRVYNbkZlCy++KFE7GZNTbVNvLW/hN87ZPTWTB7vATKQgiRhKRnFiKBmrucdDm9eH0GHp+Oy6vj9PjocnrpcLhp6XJxuN3BR83dvHekk163D4tJcdHkMVxzfjkTx2Qn+r8ghkgpxb/NqWDm+Hw2vHGQb2zezf1/eI8Nt13MnDOKEt08IYQQISRAFiKB7v/9e/zpnWOn3Kcw20J5oZ1Pnl1KZUUh1ZOLyLXJqZuqPp43lo9NK+bdwx28treVs8flJ7pJQggh+km6NG/DpZRqAQ4muh0jNBY4nuhGjIJU+H8cNwzjqkS8cYyP1WT+3Uvbhm8s8EGKHavJ+ruMJpXaCsnf3nTtV4cr2f9OQ5XO/48hH6spHyCnA6XUdsMwqhPdjpFKl/9HKkrm3720bfiStV2nkkptTqW2Quq1N1Oly99J/h9+srJHCCGEEEKIEBIgCyGEEEIIEUIC5OTwSKIbMErS5f+RipL5dy9tG75kbdeppFKbU6mtkHrtzVTp8neS/wcyB1kIIYQQQogwMoIshBBCCCFECAmQhRBCCCGECCEBshBCCCGEECEkQBZCCCGEECKEBMhCCCGEEEKEkABZCCGEEEKIEBIgCyGEEEIIEUICZCGEEEIIIUJIgCyEEEIIIUQICZCFEEIIIYQIIQGyEEIIIYQQISRAFkIIIYQQIkTKB8hXXXWVAciXfA31K2HkWJWvYX4ljByr8jXMr4SRY1W+hvk1ZCkfIB8/fjzRTRBiSORYFalCjlWRKuRYFbGS8gGyEEIIIYQQo0kCZCGEEEIIIUJIgCyEEEIIIUQICZCFEEIIIYQIIQGyEEKIpKbrw1p8LoQQI2ZOdANSia4btPa4cXt9WM0minOs6LpBc7cLj0/HYtIozbVhNsvnDiFSlcfjo7nbhVc3MGuK0lwbFosp0c3KWC1dLq5f/zpTxubw8A0XkG2Vy5bIXNHiEE1TSd+GZGj3cElPM0S6blDf1MXtT26nsc1BRZGdR5dUYzErbn7s7eC2dYurmFmWJ0GyECnI4/HxQXM3d26oDZ7TaxdXMbM0V4LkBHB7de54cjuH2xwcbO3h9ie3s/G2SxLdLCESImoccmM1M8ry4hZser069c1dLH2qdshtSIZ2nw6J4oaotccd/OMCNLY5uP2p7TSccIRtW7ahluZuVyKbKoQ4Tc3drmBwDP5z+k45pxPm7x+2sLOhnds+NoUvVE9k20et7GnqSnSzhEiIqHHIk9tp7XHH5f113eBIhyMYHA+1DYlu9+mSAHmI3F5f8I8b0NjmINtqitjm8enxbJoQYpR4dSPqee6VObAJ8df3m8i2mrj0zGIum16CpuC3/zyc6GYJkRADxSFury8u79/a46a5yzXsNiS63adLAuQhsppNVBTZw7ZVFNnpdfsitpmT+JaBEGJgZk1FPc/lnI4/XTf46/tNVFYUYDZpFGZbObe8gN/tPIJhyAcWkXkGikOs5vhM/3J7fbT2uIfdhkS3+3RJgDxExTlWHr2xOvhHriiy88iSKsqLssK2rVk0hxxbcv/RhRDR5WaZWLu4KuycXru4itwsOafjbffhDo53u5lzRlFw29ypY2lsd/Dekc4EtkyIxIgWhzx6YzXFOda4vL/VbGJzbQOrFlaGtWH9kqpTtiHR7T5dskhviDRNMaMsj+eXz8Pt9WExa5g0cHl0fn3HJfh0A00pzCbIz0ruP7oQIro8m5UJhQbP3HFJMItFllUjzybndLz974ctKOD8iYXBbedMyAeg9mAb55YXJKhlQiRG/zgk3tkgiuwW/s/Vs+hyeXns5gvpdfsoyrYwocAe1oZoGSsS2e7TJQHyMGiaoiTPFnVF5qqFlTzx2n7u/tQMyvIS3VIhxOnQdYOj7S6WhWSxWLe4igJb8nfm6ea9I52MK8giL8sS3FacY2VMjpUdh9q4ae7kxDVOiAQJxCHxpusGe1q6w+Ke9UuqmFBgD8vadaqMFYlo90jIFIvTEG1F5orNu1lYNTElVmYKIaJr7j4ZHINkpkmk9450cMaY7LBtSinOKs2l9mBbglolRGaKFvcsfaqWNodn0P1SNS6SAHkIdN2gpcvF4bZejrQ7cLi9UVdkFtotNLb5n2/pckn1JyFSjMenR89iIZlp4qrT6aGhzcHk4pyI56aV5tLY5qClSz60CBEvQ8lEoesGDk/0+Mjt9YXFUqkQI0mAPIjA7YJr12xj3qpXuX7963h1I+qKzHaHh4oiO+8f8+9f39SV9AeAEOIkq1mLem5bpPBPXH1w1J/reFJxdsRz0/vmsO04JKPIQsSLZZC+MRAr7W3uGXC/0FgqFWIk6fUHEe12wQ///D4/u2FO2IrMVQsrg6s7123dm9K3FYTIVBqwuiZ8hfbqmkrpKOOs7kgHAJOijCBPLs5BU/BOY0e8myVExjJrKmrfGEiBGYiVfvLynogsF4/eWI1ZUyk39UIW6Q0i2m2Fl+qa+coV07hvwSzOHpeH2aShMFhYNZEfvVjPzoZ2IDUSYQshTnJ6dR54oZ77Fsyi0G6h3eHhgRfq+fEXz0900zJK3dFOCuwWirItEc9ZzRoTCu3US0U9IeLG4fZF7RsfvuECyDkZKzW2OfjRiyf3qyiyM77AztEOx6BTNJKNBMiDCCS4Dv3DVhTZOdLhZOWWOp5fPo+SPBstXS5WbqmL2C/ZE2ELIU4ya4qWbhdLn6oNbpNCIfFXf6yLiWPsKBX9915eaKf+mATIQsSL1WyK2jcGYpzQWGlnQztLn6qlosjO88vnoWlqwFgqmWMkuXM4iGgJrgPTKUITXadqImwhxEmlubaohUJKc1MrPVGqO9Day7h8+4DPnzEmm4YTvfS6vXFslRCZa7AYZ6TPJyMZQR5E/8TcSilMCr5/bWVYoutEJ/AWQoycxWJiZmluWKGQ0lwbFkvyjnKkm/ZeNx0OD2X5A38omViUjQHsaepmdkghESFEbAwW44z0+WQkAfIQDDUxd6ISeAshRo/FYqK8KDJ7goiPg629AIzLzxpwn4ox/lGo+qYuCZCFiJPBYpyRPp9sZIqFEEKIpHGgtQeAslMEyGV5WVjNGh/KPGQhRIxIgCyEECJpBEaQTxUga5qiQjJZCCFiKO5TLJRSdwNXA18EngcKgT8ahvFNpdTY/tvi3b5odN2gtceNruv4DDAMg2ybRq9LD85TtJk1dMBqUnh9Bi6v/zmLSaM01xZWq1wIkbycTi+tDnfw3C62W8nKktlo8XKgtYfiHCvWQfrM8YV29rV0x6lVQqSPQEwzlLnAoftazBpWk8Ll0fHoBnpfH5llNVFo9y+2i/a6/V/DrCkcbh92qwmvbuDx6kk5Jzmuvb5SahJwM9ACfA34I/AAsFMp9Uvgxv7bDMP4MJ5t7C9QHeahv9Rz09wprNi8m7lnFrP40kks37iDxjYHFUV21iyawx93HaameiJOj86dIc+tW1zFzLI8CZKFSHJOp5c9rT3cuaE2eP6uXVzFtOIcCZLj5FBrL6WnWKAXML4gi9c+Oo7T4yNLFlEKMSSBmCZQtCOQTWJGWV5EcBpt37WL5qAbBl9+emdw20PXz2ZcQRbdLl/E604ryWVPS3fY9tU1lTy/4zDXzinn3k27B21HosQ7Yvsf4Ft9318B/MUwDB34G/CJAbYlVKA6zMKqiazY7P9D3n7ZmcHgGPzJrpdv3EFN9Rk0tjmDwXHguWUbamnudiXyvyGEGIJWhzsYHIP//L1zQy2tjuSt9pRuDrT2nHKBXsC4/CwMTk7JEEIMLlp14IEq2kXb986NOzjR4wnbdvezu3B5jaiv29ztith+76bd3H7ZmcHgeLB2JErcAmSl1A3ALqCub1MxEKgV2gmMGWBbtNe6Qym1XSm1vaWlJXaN5mR1mEK7JfiHNGkqakUYk6bItpqiPuf16TFtp0hO8TxWxch5dSP6+asbCWpR/CTDsdrj8nK8203pEALk8QX+ffYfl2kWmSYZjtVUFa068EAV7QbaN9tqitimKQaMfQaKl5K9sl48R5AXAPOBXwNVwFigoO+5AuB431f/bREMw3jEMIxqwzCqS0pKYtroQPWXdocnmODapxvB7wMqiuz4dINety/qc2aTTK/IRPE8VsXImTUV/fxNklt+sZQMx+rRDv8Fs2QIhVnG9QXI+473xLRNIvkkw7GaqgIxTaiBKtoNtG+v2xexTTcYMPYZKF4aajsSJW5Rm2EYNxiG8S/4F+fVAj8DrlRKacDHgVeBl6NsS6hA9ZfNI1GUEAAAIABJREFUtQ2sWljpnyfz932sWTQnrCLMmkVz2LT9EBVFWazt99w6qcQlREootlujVtIrtidvtad0crjdCUBx7uC/72yrmaJsC/tbJEAWYqiGU9Eu2r5rF81hTI4lbNtD18/GZlZRX7c01xaxfXVNJY/+fR+rayqTurKeMoz43jpUSk0Gfs7JLBZFwB8Mw/hWSBaL4LbBXq+6utrYvn177BrMyVWcipPZKXJtJpyeflksDDBpoBvgliwWySphQ4HxOFbFyCVRFouMO1Z/9dYhvvWbd/jply5g7BAGFb675T1yrGY23Tk3Dq0Tp5Bxx2qqiJaxAqJnmxjs50eaxSKQCcyn62hKkWMz4fbFPYvFkN8g7r2+YRgHgE/2PfxYv+eO99+WDDRNUZxjPeXKz4FXhmYnzYpMIcSpeb06H7X2sCwki4VkoYmfI+3+uYxF2UMbRRqXb2dXY3uMWyVEajpVxoqhVrQbbvW7aPsOJYZKRtLjD9FgKz+HszJUCJGcmrtdweAYJAtNvB1pd1KUbcU0xAvmuIIsTvS46XB4YtwyIVJPMsUlydSWoZIAeYgGW/k5nJWhQojk5BlgxbVkoYmPI+2OIc0/Dgikgzskqd6EiJBMcUkytWWoJEAeosFWfg5nZagQIjlZBlhxLVlo4uNwu4PiYSxoLusrKHLwhCzUE6K/ZIpLkqktQyW9/hANtvJzOCtDhRDJqTTXxrp+WSwkC0186LrB0Q4HY4fRZ5bm+UeQpViIEJGSKS5JprYMldROHSJNU8woy+P55fOirvwc7HkhRPIzmzVmluXx7NJL8fp0zJKFJm6O97jw+IwhZa8IsFtNFNgtMsVCiCiSKS5JprYMlQTIwzDYas7hrvYUQiQfs1ljQqF98B3FqDralwN5zDDmIIN/moVMsRAiumSKS5KpLUMhAfJp8np1mrtdeHy65DoWIo3IuZ0YR9r9C3iKc4Z3AS3Ly+KjFik3LcRwRMuPPNLR3Fi8ZiJJgHwavF6dD5q6JFeqEGlGzu3EOdbZV0VvmHMSS/Oz+MdHx3F5fdiSeMGPEMniVPmRTzegjcVrJpr0+KdBcqUKkZ7k3E6cpk4XZk2RN8yqhWX5Ngyg4YRj0H2FELHJSZyKeY4HIyPIQxB620ApdcpcqUfae9ENsGgKk6bQDTAMA5dPJ8tiYmyObcifptLtdoUQyU7yICdOU6eTohwrSg2vjysL5EI+0cNZpbmxaJoQKU/XDY73uHB6fBgGUfs5h9tLU4eO0hQmDXpdPqwmDa9uoBQYBvgMA00pbGYNQzdw6wa+vpLTc88sZv6sMgrtFtodHtZt3ZvUeY4HIwHyIKLdNth428VUFNnDDrCKIjtdTi/tDg9PvLafW+ZNoTjXimEYOD069/++jpZu15BvOaTj7Qohkp3NrEU9t60yvSLmmjqdFGVbhv1zgQBZUr0JEV3/eOKxmy+M2s/tbenBataCMczzOw5z7ZxyHtu2n5vmTmHF5t3BeOSXN1fT0evh7md3BbetWTSHh1/Zw0t1zVQU2VldU4mm/O+finGL9PqDiHbb4Pt/rGPNojlh+fxW11QyJtfKis27WVg1kXs37eZwmxOTZuJEj4dll08d1i2HdLxdIUSyU8DqmsqIczv1uvbUc7TDX2Z6uPKzzNgtGodOSIAsRDT944mfvLwnop9btbCSn7y8JyyGuf2yM7l3k/9xIDgGfzxyuM0ZDI4D25Zv3MHCqonBx/du2s3BE46UjVtkBHkQ0cojvlTXzFeumMZ9C2YFbyU88EI9P/7i+TS2OSi0W2hsc5BtNaEpyLaayMa/eGSopRVTsSyjEKnO6dV54IX6qOe2iK2mTiczxuUN++eUUpTmZ9EgAbIQUfWPJ3Y2tPPAC/X86vZLONLuoN3h4Ucv1rOzoR0gGMOYNBUW04TKtpqixiiFdkvYY9X3/qlIAuRBBMoj9r8VcbzbzdKnasO2mTTFlbNKaXd4qCiy0+v2oRvQ6/bh7pvDONTSigO9bzKXZRQi1Zk1RUu3K+LcNqfg7cFU0u3y0uv2ndYIMkBJro0DMsVCiKgsUaaOtXS7cHl17nluV0ScEYhhfLoR9jh0v163L2qM0u7whD3udftSNm6RKRaDiFYe8eEbLqBijD3i9sR3//Aed10xjR0HWlldU0l5URY+3ceYHAvrtu4dVmnFVCzLKESqK7ZbWduv1PTaxVUU2+W8i6VjHX1FQk6zfyvrG0E2DGM0myVEytN1g26nN2JKxUPXz8ZmVhFxxqqFlWyubWB1TSWP/n0fq2v8j1ctDP/58qIsHrp+dti2dYur2FzbEHy8uqaSScXZKRu3qFTvUKqrq43t27fH9D36Z7EwKbDbNLocPo52OGntcbNu6152NrRTUWTnmTsuwRyRxcIgy6JJFovES9gvMB7HqhiZli4Xf9zVyBWzxmMYBkopXqk7ytWzKxJRASpjjtXXPjrODT9/k/uuPptZEwqG/fMv1R3jsW0HeOs/51Pat2hPxFXGHKuppqXLxbVrtlGSa2PZ5VMptFvodfs4Z0J+sKx7//jmZBYLHatJ4dUN/2I7A/S+fjE0i4WuG2RZTIzJtnLC4cbp0f1xktVEoT3p4pYhN0amWAzBQOURuxy91Kx7PWxb4HZDWcHIS9WmWllGIVKd2+vj/i0fcP+WD8K2f+qc8QlqUWYIFAk53SkWpXl9mSxO9EqALESIwPzjxjZH2NSxbSs+EQxcB4ozinOG/36BczEdyBSLEQjMEw4l84SFSF1yTidGU6e/EEvRaU+x8F/gD8k8ZCHCSJ92+iRAHgGZJyxEepFzOjGaOp1kW01kWU7vol2Sa0OBpHoToh/p006fTLEYAU1TzCjL4/nl82SesBBpQM7pxPAXCTn9C7bZpFGca5UAWYh+pE87fRIgD0G0xXK6btDc7cLr0zFpCqX8c33aev2pUwIlGS2axtgcK21OD06PD00pLJrCZtFw+ww8Xl0OWCGSiNvtw+314dUN8Ppwu31kZUlXGUvNXS4K7MOvoheqLD+Lg609o9QiIVKTrhu0O9w43D58hn/x3NgcW3CecWjJ6YHikQKbiZYeN96+EtJ2q4bL61+MZ7ea8Or+fS0mDa9PxxvyPukUx0ivP4hoJZ9/dfvFdDi8LNtQG9y2amElT7y2n69cMQ2AOzfuoLHNwZWzSvnK/OncGbLvz264AE2p4D5SRlqI5OB0etnT2hN2vq5dXMW04hwJkmOoqdPJpDHZI3qNsvws/tlX6ECITKTrBgdae2jqdHLvpt0R8QUQEc+srqmkONfKj16s56W6ZpZ+bDILzq+I6AO3/LORtw60842rZnDvpt2U5NqC36drHCNzkAcRreSzy2sEg+PAtkB5xjs37uB4tzv43MKqicEDLbDviR5PMDgObJMy0kIkXqvDHXG+3rmhllaHnJuxYhgGLV0uCkcwxQKgLM/GiR43XU7P4DsLkYZae9wcbO0NBq0QHl9Ei2fu3bSbw23OYInomuozovaBNdVnsOzyqcHXDv2+//ukCwmQBxGt5LOmiNjWv8R0wHBKNKZqOUYh0oVXN6Kem149tfPFJ7NOpxeXVx/RHGTwjyADHJRMFiJDub2+U8YX0eKZQMwSKBEdKC/dfx+TpsLimWixTbrFMXLPcACBecdARDlF3YjcFlqOsdd98gAJbAtN0l2ca+PKWaW8VNcc9vOSdkWIxDL3lYtfWDWRQruFdoeHzbUNUmo6hlq6/DmQC7NHOAe5wB8gHzrRy7nlwy82IkSqs5pNwRLQ/QuD2K0mfHr02KXX7cPt0wGC5aX77+PTjbCS09HKT6dbHCMBchSh845Lcm2srqkMm2djMyvWLa6KmIP89/omnl16CYYBW79+OQCdTg8bb7+YI323MkLn9AC8VNcsaVeESBLFditfnT897NxeJ6WmY6o5kAN5pAFyX4GCA7JQT2So4hwrk4qz+cVNVbi8BstD1zktqaY038r6xVUsDenfVtdUBhfw/eXuy/AZBhtvu5jv/7EuGJ+sXVyF26tTmmfjyVsv4od/fp91W/dGxEbpFsfELUBWSpmBXwETgHpgObAJmAjsBm4EbP23GQmohR06T6exzcEDL9Sz8ppzmVqai93izzhRmuPj2Tsuwa0beH0GhqHzpUsmcbzbHXZQrq6ppDDbEjFX584NtTxx60V8+RPTKM61YtEUrT1uyWYhRAK1uzwR6wuWbahl07JLGSeL9GKiKTiCPLILq91qosBukWIhIiNEy64FYDEpTJqZf3/izfD5wU9t5/FbLuKJ1w7wo+tmM74gC5OmMAyDDa8f4LIZZazYfDLYXb+4ivs/d04wI9f3ttSxsGoiPt3g21fP8hdsNmDjbRejgCyriTF2a0SbUjmeiecc5M8DuwzDmAeMB+4CGg3DmA0UAZ8CFkfZFnf95+nsbGjnlsffxqT8JRl9Pp0Pj/dwqM3B4p+/ySf/+28c73aj6wSDYzg5Ad7tjT6vsdPhodvl5YuPvMHF//UK167ZRn1TF7rMdxQiIVxefYB5dXqCWpT+AiPII51iAVCaZ5M5yCLtBe5yX7tmG/NWvRqMHY73uDjc7qSlyxW1H2vvdXPNBeWs+vMH7GvpYU9TN1969E3mTC4OBseBfZduqKX+WDfvHunke1vquGnuFFZuqaNm3ess+vmbdDm8/OyVj/j46q2YNMXYHBt7Wroj2pTK8Uw8A+QXgP/uG0kuBOYAf+l77hXgE8AVUbbF3WClGZu7Xdy5oZaxudbgATUuP4sTPe6oB6VJEfX1cm3miIMy3VaBCpFKTJqKeq6m8ihIsmvucmEza9hPs4peqLL8LPYflykWIr1Fy0Zx+5PbcXp8jM31j+JG68dae9ys2LybZZdPJdtqCi7oG2jBXWDx3sKqiRGxyrINtdx+2ZnB2GigNqVyPBO3ANkwjG7DMHqBbUATUAx09D3dCYwZYFsEpdQdSqntSqntLS0to97WwUozBla6m9TJi6nPMAY8KI93u/nZDXPCXm/VwkqcnugrStNpFWimi/WxKkaXWVOsrqkMO1dX11RmxCK9RB2rzV0uirKtKDXy3/G4giyOdTpxuKUPTWeZ3q8OlI3CpBQmTbG5toFVC8P7sXWLq1i3dW8wIO51+4IL+gIL7kIFtrc7PBTnWKO/n6aCsdFAbUrleCaec5CLgW5gLv7R4bOAwFLjAuA4kBtlWwTDMB4BHgGorq4e9fH7wUozmvtGmY51OoOT1I91OIMHZeg8njWL5qAbBhvfOMh9C2ZRnGNlXH4W393yHgurJqb9KtBMF+tjVYwuDX8axpXXnEu21b8iPNtqyoh8mIk6Vps7nRSMwvQKgPEFJxfqnT0+f1ReUySfTO9XA3e5+8cOdqsJzQO3zJvCY9v2B2OOMTlWupwedja0B7NWTCjMwuvTWV1TyWPb9kfELqtrKnnghXoAHvrC+VHfz2bWmFycg6apAduUyvFMPPv9e4DrDMPwAb3A94Er+567AngVeDnKtoTQNEVJno3yomxK8sLLJ5bm2li7uIrHtu3H3ncxLbBb+MoV03jiNf9BuWnZpWy87WJK86ysefUjnq1tZOWWOgzDwMDge9eex/kVBaxfUjXgSLUQIr7ybRZybOHjBjk2M/m20QngRCT/CPJoBcj+vlSmWYh0NtBd7kK7lZLcLErybHzpokkU2i10Ob10OT3c//u64Ehyrs3ML/53f18paY1vfeZszhhj59d3XMI/VnyC3yyfS1l+Fi3dLnY2tLP1g2OsW1wVMSJdlpcVjI0Gu/OeilS8kkQopcqBpwA7sBf4d2AzcAawC38WC2v/bYNlsaiurja2b98ew5ZH5/H4aO52oRQYBhj4R54CNcqVUpgUWMwaXp+B06tjUv6V1oX2k6PR0VaiynzHmErYLzdRx6oYHqfTS6vD3XfxUBTbrYkqM50Rx+o533mBj00r4aZLJ4/4tZweH7c8/jb3fnoGX/7EWSNvnBiqjDhWk8mpYofQ5yxmDbOmcLj9+xXZLbQ5PLi9/tzIgZjlVK9hNZsozDLT0uPG69MxmzRKc22YzdqQ25REhtyguPX6hmEcxj8qHGpBv8euKNuSksViorwoe8SvExipFkIkh6wsM+WS0i0uet1eelw+iuyjM4KcZTExJsfKvhYZQRbp7VSxQ9Tnck5+O5SYI9prTCi0D7D34G1KRZkwtU4IIUQSOpnibfRuw47Lz2L/8e5Rez0hRGYaUYCslJoVZdu/jOQ1hRBCZIbmrtHLgRwwviCLfTIHWQgxQsMOkJVS5ymlzlJKTQYe7Pt+ulJqglKqBvjOaDdSCCFE+mnuq6JXNJojyAVZtPd6aEvh/KtCiMQ7nRHkP+FP03Yb/pzFXwf+CiwCvgY4Bv5RIYQQwm80q+gFTOjLZLFPplkIIUbgdFaifIQ/acP7wJn4U7HNAA4CntFrWvpKkZWeQmQkOT/jp6nLiVlT5NpGb1FkeV+aqQ+buqmaFLXWlBAZRfq00zOSXsnAny6jEhgPfBKYAHSNQrvSVqCGeqAkYyBX4IyyPDlghUgwOT/jq6XTRWG2ZVSq6AWU5NmwmTU+bJJLkRDSp52+kQTIgd9sN+AGTuBP0+YaaaPSSf9PbiaN4IF6wcRCll0+lR6Xl2OdTsblZ0U9YJPp018ytUWkt0Qca609bn67o4HHbr4Qk6bw6Qabth/itsvOSqv0Rcmiucs1qhksADSlKC+081GzTLEQ6WMo/aGuGxzvceH0+DApFcxzHIg5wF/++fYnt/P88nlR+zS5xp800hFkA3/Rj+PADuBiMngEOfTAUkphNSmaulwsfaqWklwbKz4zk/EFWWy87WJO9LhQSnHX0ztP+akumT79JVNbRHrzd/RO3F4Dn25geH0c73EyNif6h8jRojC4enY5tzz+dli5eEXGVbONi6ZOJ4WjlAM5VHmRnfpjGXspEmnG69U50uGguctFa4+bzbUN3P2pGUwryQ0r+tHU4eKhv9azsGpisMR0jtUUVv45MDDX6/bS0kVEcRC5xp90Oov0ZgJnA+fjD7A/0/f4LCBj67EGDqxr12xj3qpXuX796xxud/I/f/2Qklwb3/zMTL7+3C4+vnori37+Jk6PjgJKcv2f4AKf6lr7rbxu7XFH/fTXf794SKa2iPTW7XbR1Onmi4+8wcdXb+WLj7xBU6ebbndsb1C5vDrLN+4IO8aXb9yBy6vH9H0zVSxGkAEqirJp7nLR4ZBlMSK16bpBfXMXN/z8TWrWvc7KLXXcNHcKv93RQH3zyZhjV0MHD/21npvmTmHlljpq1r3Ojb98i5ZuN1fOKgX8wfHXPz2DlVvq+PjqrVy7Zhv1TV3oun8AQK7x4U4nQJ4DzAb+CyjAn8WiEvgp8DPC6rVkjmgH1pef3sHCqoksu3wq9zy3K+y5ezft5kSPh2WXTw2+RmObA7fXF/a6bq8v7NPfQPvFQzK1RaS3LofOnRtqw86ZOzfU0uWIbaDq1Y2ox7hXlxHk0eby+uhweCgaxQwWARV9Fb/2yDxkkeJae9wsfSq8L1yxeTc11WeEbc+2mlhYNZEVm3eH7btsQy3fvnoWFUV2ll0+NeL50ABYrvHhhj3FwjCMo0qpMqAUuLdvW4tS6t8Mw/iVUuqD0W5kKnB7fZTk2rhvwSwK7RbaHR7Wbd1LcY4Vk6aCB13g9kah3UJpng2rWeP6qgqerW2kosiOxazR0uUKzv+xmDUqiuxhB21FkR2r2RT3/6PVbEqatoj05tUN5p5ZzO2XnRmcC/zo3/fFPFA1ayrqMW7OwNuLsdbSNfpV9AIqQjJZVE+WTBYidQ0UtJpNWjDeKMw2k2uzUFaQFRaH6IZ/ihrAr++4BJMi6ms5PD4Ot/WiVPT+L1Ov8adbSe9s4DOGYewGnunbtgzAMIydo9GwVJNl0fjGVf5bF1945A1WbqnjG1fNYHxBFgV2CxVF9rDbG1945A2W/PItWnvc3HbZFJZ+bDKP3lhNt9MbvGVy7ZptdDu9PHpjdbDDD8wJKs4Z/YvKYIpzrEnTFpHecqwmFl86iVsef5srHvwbtzz+NosvnUSONbYddWmujbWLq8KO8bWLqyjNlQV6oy0WVfQCxubZyLaaqDvaMeqvLUQ8BQamQlUU2dEUrNxSx1v7WnH74AuPvMHRdkcwDvnhn/1jld/8zTvBaWqtPZ7gdIvQ19rb3M28Va9y/+/fZV2//i+Tr/HDHkFWSv0eGAvkK6XmA5VKqT8As/v+tRqG8elRbmfSc3kN7t20O2IaxYZ/v4gf/vl9HrxuNr1uX8TtjeUbd7DymnO5ce4U7FYTn3t4W9jzN/7yLX5/1zyeXz4v4atKNU0xoywvKdoi0ttAc4GfueOSmL6vxWJiZmkuz9xxCV7dwKwpSnNtWCyZOYISS82d/ip6Y2Jw8dWUYnJxDu80SoAsUltgYCp04dz6xVV8/491NLY5uGZOBTc8+gaNbQ58usE3f/MOjW0O7lswKyImWbahlo23XUzd0a7ga62uqeSBF+oBeKmuGYBnl16KYRgZf40/nSwW1xmGIanc+vH49Ki3LnTDf9C1dLn50XWzo+6TbTXh0w0c7ui3UhxuH+VF2TH/PwyFpilJdyViLpFzgS0WU9Kcb+ksOIIcgywWAFPG5vCXuiY8Ph2L6XRvlgqRWNEGpnRdDwazunGyr7SYtOD3hXbLADGJwVO3XkRzl4vyIjtfeXonOxvag/u8VNfMdz5rSB/I6U2xeF4ptU0p9Y5S6vdKqea+f4/1/funUW9lCgjMXQxVUWTHpxtUFNnZ2dDORy3dUffpdfswm7QBb6Vk6vwfkbkGOp9kLnD6aOp0oinIj2GA7PbpUjBEpLzAwFR5UTYleTY0TQv2j5o62Ve2OzxRvw/wxyTwYXM39zy3C00pWrpdEftIzOE37ADZMIx/Bf4v8BjweeB9wzA+B+w2DONzfc9nnGhzF1ctrGTT9kOsXTSHiiI767bu5aHrZ4fts7qmkolj7JTm2mSOrxB9iu3WqHOBi+1yLqSLpk4XRdlWtFGsohfqzLH+hErvHpZpFiK9hMYKv9vRGOwr123dy+qayojvgWBOd6/uY3NtA4/eWE1prk1ijlNQhjH8W5ZKKTvwTcMwvqOUWgr8ArjVMIxHRruBg6murja2b98e77eNyuPx0dztwqcbmDSFpgH4C4YoBQ63HpzXaDVr9Lp9WDSF3arR49bR+35OKdANsJo0xmRbMZvl9uAoStgQZDIdq6nA6fTS6nAHz5liu5WsrJHUNko5aX2sLvnFmxxtd7Dy8+fF5PV1w+C2J7azsKqc78XoPURQWh+r8RQoOKbrOkopDMPAo/uzUVjNGlazwunW/VXyfIb/N2+A26ejKTAM8BkGmlLYzBoerz/uMGmKLIuG12egaVpwbrHXq9Pc7QpORSrNtaV7zDHkY/V0Fun9AugFPq+UKgLOAy4HjiulfgJ8aBjGw8N93XTQf+5ioHjIm3tbqJoyNpjXNfBJbsPrB2l3uLnrimnBBUmBkecnXtvPLfOm0J2fxeTinIydJC8yk9PpZU9rT9g5s3ZxFdOKczItSE5bTZ3OmKR4C9CU4sySHHYeah98ZyGSQCBmeOgv9Sz/xFkooNftCy62qyiy89D1s/nBnz6gpdvF07dfjMvj43i3m8e27eemuVOCiQBCY4mb5k7hidf2c/enZoRVxdN1gz0t3VI5bwCn8zHhB8CPgMa+f28H8oDVwH8DNyql0vrjx2B03aCly0Vjey/HOpx8+rwJHO9y8eB1s1m/pIqSXBvLN+7grvnTuPHSyRGr9Vds3s23PnM2To9Oa7c7Y6vYiMzV6nDz05c/5L4Fs3jmjku4b8Esfvryh7Q65FxIF02dLopifCt35rg86o500tErFfVE8gsUHFtYNZG2Hg8nejwRmSjufnYX91w5nfsWzELXwaRp3Ltpd1iRkAsmFnLfglnYzBr3fnome4518H8WnIPNrHG0w4Hb7Q2LUQar6JupTqdQyF4ApdR/GoZxqO/7tSHfzzMMI2PrsvavZX7lrFK+On869/3u3bBPdT96sR6vT2d8oT3qStPmLhf3/e5d1i6ag65n7K9TZCiliDoaEqPpqiLOnJ5AFb3YBsizJhSwecdh3tzfypXnjIvpewkxUoGiIKGZXaLFB+ML7dz0y7dobHOwadmlwZ8JBMdf//SMsL5z7eIqvrflPV6qaw4+/unLHwYfB2KSnQ3tGV05r7/THuk1DOPVkO//GPJ9Rn9U719yemHVRJZtiCwT+dX50/DpBodae6OuNG13ePzldTfuwCdVbkWGMQwicoav2Lyb01gyIZJQoIpeLMpMh5pWmovVpPH6vtaYvo8QoyGQyard4aHX7aPX7YsaHxxq7Q32ja097uDPDFRO+s4NtSysmjjg4xWbd7Ps8qnB15csFn4ZPRUiFvqXhRwoF+Hksdk8+vd9/OTlPRGVa1YtrGTd1r3BfU9nIaUQqcxnRM+DrMu5kBaa+oqExHoE2WLSmD4ul9f3SoAskl8gO8Xm2gaKciyMybFEZKJYu7iKn7y8J/gz67buZdXCSjbXNrBqYSXFOdaofWf/UelojyWLRThZ7TLKAp8AAwdo4FNd/9rmrd1unq1tpKLIjm4YPHbzhWia4lBrb/BWR2Bf+TQnMo2pL7dn//MmVinBRHw1dcauzHR/54wv4JntDTR1OinLz4r5+wlxugJFQb5/bWVYFotf33EJum5gMWuY+uUu3tnQzhOv7efeT8/E6fFRmG2N2ne2OzynfFxRZOf55fMyunJefzKCPMr65zLeXNvA+n4jxKtrKvn+H98Pfm+3mlj94gd8/dldZFm04MEvn+ZEppI8yOmtuatvBDkOfdtFU8YA8IddR2L+XkKMVKAoSFmBndL8LMoK7FQUZXNGcQ7jC+yMjZK7+JZ5U/jGpt3cuXEHJ3r8CQH6952baxsGfPzojdWML7D3FSHpU/0GAAAgAElEQVSR4DjgtPIgJ5NkzIEYyGMYKAtZZLdwoteN0+PDpCnMmsLt8386tJo0bBZFt9OHz4Acm4ZPB49Xz/g66DEi+TpThORBTt9j9b/+9D6/+Md+nrz1IlQc7gp8+/l3yLGZ+cNX/iXm75Wh0vZYTUahMUagjLrT4//eala4fQZ6X+5ks0mj2G7heO/JvrQkx0qHyxeMUTIszohdHmQxuMAnwFClg9zaK7Cf8mkhMk5WlpnyzAqIM8bRDifFuda4BMcAl04tZuObh9jX0s2ZJbkD7ufx6Ww/0EZLt4uSXBsXTi7CbJIbrSK5RIsxBlNuC+9LS6zStw5GfkNCCCHi6miHgzFxnDo2d+pYnt3ewM9e3cuD18+OeN6nGzz95kEe+useToTkgB2ba+X/+9QMvnjhxEwaYRNCEMcAWfmHCh4HZgDNwA3Ar4GJwG7gRsAGbArdZqT6HJA+/addFOdY/QVFely4vTrmvqkXLq+OSfOXiNQ0hVc3MHQDnwGGYWTi7RCRoWSKRfo61uHkjDHZg+84SsbkWLnqnHH8Zkcjt8ybzLnlBcHnGk708rVn/kntwTZmjc/n5ksnM6HQzuF2By+8d5T/fP4dtuw+wo+/eD6lebLIT4ye0LLSgWu83WrCqxt4vDqaUmgKlAYerxHsC61mLbhor9Au8UCsxPNqMw8wG4ZxiVJqK3Ar0GgYxgKl1BbgU8AZUba9FMc2xkT/4iEVRXaevPUiHB4fS586WUp3dU0lD7xQT0u3i7WL5mA1Kx586cOIgglSClKkOyk1nb4Mw+BYp5PzJxbG9X2vOb+cv33YwrKnavnFzRdSXmTnue0NrH6xHsOAOz8+lY9NGxuc9lFeZOfCyUW8Wt/CE68d4Or/+QcP33ABF59ZHNd2i/QUWlY6cI0vybXxjatmhJWWXr94DjoqrC9cs2gONrPC6dFpz/IwuThH4oEYiOfkqibgf/q+dwP3A3/pe/wK8AngiijbUl7/4iGNbQ4OtvYGg+PAtns3+ZN1BwqEmDRTWPnIwH5SClKku1aHO3hBgJPJ7aXUdOo70ePG4zMYkzO8OZQjlWMz842rZtLp8vDpH/+d877zIv/vD3VMLcll1cLzuGx6ScScaKUUV8wsZeXnz8VsUnzp0Tf4rz+9T5czsh7W/uM9/OIf+/nKr3Zyw6Nv8PXndvHie8ckj72IKrSsdOAav+zyqRGlpZu7IvvC5X3xwYkeDwdbeyUeiJG4DcUYhrEHQCl1LWAFaoGOvqc78U+9KI6yLYJS6g7gDoAzzjgjdo0eJf2LhwBkW02nTObd2OZAUwMXGpFSkKkh1Y7VZOHVoxcK8eoSbMRKvI7Vox3+FG+JSF85tSSXH3z+PN46cIJOh5cLzihkWmnuoIsFzxiTzfc+fy5Pv3mI9X/fx8Y3DzH/7NJgTvu3D5xgb0sPACV5NgrtFt490sGm2kYunFzE+iXVcZ1zne7SoV8NLSsd6OuiXe8HihU05X8u8Fpi9MX1XqVS6nPAfwCfBdYBgYlgBcBxIDfKtgiGYTwCPAL+FC8xbPKo6F88BAiWkBwombe/gMjAhUakeEhqSLVjNVmYteiFQsxyGzFm4nWsHusLkMfkJiZgLM618Zlzxw/757KtZm772JlcMbOUP797jP/dc5z2Xjc5NjNTS3K56dJJVE0qoqRvnrJPN/jbh/7pGTVrX+PZZZcyNje+o+bpKh361dCy0oG+Ltr1fqBYQTf8zwVeS4y+uE2xUEqNA+4FrjYMowt4Gbiy7+krgFcH2Jby+hcPqSiyM6k4m/VLIguIrNu61z/fctEcfLovWD4ydD8pHiLSnRQKSV9H+8pMp+qI6pkluXz5E2fxsxvmsOHfL+aRJdWsuGomV507PhgcA5g0//SMb31mJo1tDr7y9E68Pj2BLRfJJLSsdOAav27r3ojS0qV5kX3hmr74YEyOhUnF2RIPxEjcCoUopVYAtwPH+jY9hX8k+QxgF/4sFlZgc+i2wbJYpEqS8FNlsfD0Za4YShYLi1nDalL0uHz4DIMsi4mxOVL9ZhgkoX2cRDvmh3OcShaL9DxWV7/4Aev+to8nb7koY/qtv33YzLq/7eOeT03nK/OnJbo5sZCWx2qsDTmLhQKPb/hZLEbaB6ep5CsUYhjGKmBVv83r+z12AQvi06L4ipbYW9MU44dRIUTXDQ609tDU6Qxb5SpZLUSyiZa5ZTjHqa4b7G/rPe2fF8nraIeTomxLRv0dPz69lH82tPPTVz7is7MnMHlsTqKbJJLAYAU/RtKPjrQPFvHNYiFGqLXHzcHW3ohVrpLVQiSbaJlbhnOcjvTnRfI62u5M2ekVI7HkksmYTYr7f/9eopsiUsRI+kHpQ0cuo+5Xpjq31xexovWCiYUsu3wqvW4vLV0nV4bLbRWRSNEytwwn+4rb62PumcXcftmZmDSFTzd49O/7ZLV2Gjjc7qC8aOh3ztLFmBwr115QzsY3D/HGvlYukXzKgujTLALX7ZH0oyPtg4UEyCnFajaFrWi9YGIhX//0jIgiIjazxo2/fEtuq4iEiZa5ZTjZV3JsJhZfOolbHn87LDl+jk1Wa6cyXTc40u6Ie5GQZHHlrHH8+d1jPPDCB2y+c+6g6eVEeotWLCT0ul2WbzvtfnSkfbCQKRYxpesGLV0uDrf10tLlQh9hDtfiHCuTirN56PrZVBTZWXb51KhFRA629g7ptspot0+IgGiZW4aTfaXXrbN8446I5Pi9bskCkMqau1x4dSNj051ZzRrXXlDOjkPt/O+eqFlMRQaJViwETl63vT6DX99xCa/c83H+cvdlLP3Y5CH3o4P1wXL9H5yMIMeI16tT39wVVkp6pCO5mqY4oygb3TBYec25TCrOjnoLJZA8PHRb/9sqMoFfxJKmKWaU5fH88nmnNdXHp+tRj22fLgFyKmts6wWgJC/z5iAHfHx6Cb/deZiHX/mIy6aXJLo5IoGiFQsJaGxz0OP2seQXbwav0WsXV3HW2FOXlQ7NXFGWb+M3y+fi8ephfbBc/4dGRpBjQNcNjnQ4IkpJj8YE+TaHh5sfe5tbHn+bPc3dwU+HARVF9mDy8NBt/W+ryAR+EWuBFdrlRdmU5A0vFaGmVNRjW5Nb0intcLu/v8nUEWQAi0ljQeV43jpwgrcPnEh0c0QC9S8WEqqiyM6B4z1h1+g7N9TScoprdCDwvXbNNuatepXPPbyN1m434wvsYX2wXP+HRgLkGGjtcdPc5YrJBPnQiffrtu6NWkRkUnH2oLe2ZQK/SGZKEXFsr1pYicTHqS3Q52RygAzwiZml5GeZWfe3vYluikigaMVCwN/frVtcxU9e3hO2f2Ob45TFZoYa+Mr1f2hkikUMuL0+WnvcA06QD70FopTCpMBi1oLJwQO3QiAyG0XoxPudDe386MV6Vl5zLlNLc7BbzMGfG+zWtkzgF0Ph8fho7nYFE9SX5tqwWGJ/jBgGPPHafu5bMItCu4V2h4cnXtvPdz57TszfW8ROY5uD/CwzWXE4hpKZzWziynPGsam2kT1NXUwry0t0k0SchF7/LWaN0jwr3756FjazxnNLL8Wj65iUv3BYS7crmKmq0G6h1+075bkz1MBXrv9DIwFyDFjNpuAnwtBVqeuXVFFkt0TM/XnwutlkWTS+/PTO4LYnb70Il1ePmCM0rSSXR5dUc/tT/u0t3S6yLBpur86E/JPJ90+VfBxOfnLt//pSslIEeDw+Pmju5s4NJ+fRr11cxczS3JgHyXlZGl+ZPz3ivfOy5KZXKjvc7sj40eOAT80q4/e7jvDI3/ex+rrZiW6OiINoc39X11TywAv1lORZueuKacHFyUs/NpnHbrmQ412u8MJgS6oZmxt9ytpQA1+5/g9N3EpNx0oylpkMTd2ysGoixTlWSvNsTCiw0+bwcO2abREH8MprzuWWx98Obnvs5gu573fvBtO53XPldMYV2MmyaFg1xcETDnTDoN3h4cOjnXymcgLZVg3DAN0w0JTCbjUNuQxlYCRb07R0z5ssJVGH6HBbLzsOtnLBpGJ8uoFJU+w82MqcScWUF2XH9L2PtDu4//fvsrBqYnAEeXNtA/d/7lwmFGZMDt20O1aveHArY3Nt3P3J6aP+2qnosW37eeWDZrZ98wrK8rMS3ZyRSLtjNRZaulxRr/9P3eovu360w8nYXCsmpeh0eijKttLc5aK1x83LdU3Mn1VGcY6VCYV2xuVnRVynh7P4LoPLUCdfqelMEljB//1rKyMOvoFugQQyTwRup0wqzua+BbN4ua6JhVUV3PPcrrBPnLk2M//3d+8xrTSXxZdO4oEX3o/Io7i6ppKy/CwKsy043JEngaYpinOssppVRJVt1Zhcks8XH3kjbBQ32xb7UVyPT+elumZeqmsO2/7tqyWLRaoyDH8O5Jnj8hPdlKTxr+eN56/vN/HLbfv51mfOTnRzRIwNdP3vdnlBKb4ecp1fs2gO3/tjHS/VNQcfP/zKnuDjwHUawqdiTivJHVL2oMHKXAtZpBczA63gD9wCCRXIPBEo/LFySx1XPPg3Vm6pY9nlU4PBMfhPpns37eZ4t5t7rpzO7ZedyfKNO6LmUbx3024Otvayq6GDu57eybuHOzh0opfWHifNXU4Ot/VyrNPJQ3+pl9WsIkKvWw9OcYCTq6h7XbEPUi0mLep5YjFJl5WqWrpdOD06JTLFIqgsP4uLpoxh4xuH6HR6Et0cEWMDXf9zbOaIvjZwXR/o8e1PbudYp5NDJ3p597D/Gn/tmm3saemmOMd6WtmDRDgZQY6zaHN/HrxuNoXZZlZfN5ubH3sreJKU5NrQDcI+cQZGmKeU5GDWFFaToiTXxtSSnAFHprOtpmDFvZJcG9+4akbYnKZVCytp6XKzs6E9+HOymlV4dYOSXFvYQrl1W/fijUNCebOmWF1TGXacrq6pxCydfco62OrPgTyuIKWnEoy6z80u5419J9j4xiHuvHxqopsjRlH/aQz5VhMbb7uYli4XHp+O3WKiKMeKSfNfx0Ov4YH8yAM9Lsm14fL6aO50AfCdz83i//2+jtuf3M7zy+fJ6PAokAA5zgLTL36zfC4Otw+Pz8AwdJweHZfXHTxBAqPJDSd6T1laet3iKr73+XNoOOGIOjnfALIsJu7sm/h/34JZwaAD/Cfdis27uW/BLJY+VRv8OVnNKrLMWsSHqdU1lWSZYz+K6/D4eOCF+rDg/IEX6vnxF8+P+XuL2Nh/vAeAcak913bUTRmbQ2VFAb/4xz5umTc54zN8pIv+84GvnFUaXHhckmvjP/91Jnf9amdY3/rAC/XBgapAfuSA0McXTCzkG1fNYMkv3gr7+W9cNYMvPfqmDHCNEgmQE0DTFArFop/7K+SsX1LF5toG7v30TDYtu5TWHjc5VlNwxHftojkc73YzcYydhhOO4CfNxjYHyzbUsvKac/nJy3sismasrqlkUnE2Lu/JqmQDVewJrF4NXc2awZP4BeAz/IuIQoPUx7bt5/7PnRvz9w6kOAp8aAP/sSkjyKnrYGuPf6RMRrYiXDN7Aiv/+D7PvN3ATXMnJ7o5YhQEchIH7sKdPT4Pt9fgp1+6gHy7hZt++VbYQNVj2/az+rrZtHa76HX7qBhjZ/ULHwAE13/89OUPAfjq/GkRA133btrNU7deJANco0gC5AQJTNa/YGIhM8flseIzZ3OotZefvLyHlm4XT9x6EY1t/mC41+0LZrQITIn40Yv+T5qBaRSBnMiBYGZCoZ2v/mon3/zMTP/J1je67PHpUUeaywvtbFvxibAczLJ4L9MZEQs/Vy2sBIY2xWIkH7BybCbWLJoTTHkUWKSSY5OOP1UdON5LSZ4Nk/QfEc4en8/McXn87NWP+MKFE2UUOQ24vT5Kcm18/dMzeOK1/WF96aZll0ZMnbxp7pTgFMtAWtj7P3cOK646G5Om+MM/D/Otfz2bb/3rLEyKqANduoGkaxtFsuIlQaxmE0s/NpkHr59NS5eLj5q7+dVbB/n6p2dQkmvjUKt/akW0RXorNu9mWd9ctdDS0jsb2ln6VC33PLcLA/+imHaHh5+8vIfVNf4qPaa+uZ2hFXtW11Ris2hhk/qlFKUwDCIWfq7YvJuhZIbsX/L02jXbqG/qQh/i/OX8LCtF2RYev+UiXrnn4zx+y0UUZVvIz5KOP1UdaO2hTEaPo1JKsXBOBc1dLn791qFEN0eMAqvZxFfnT2PF5t0Ri+gDhcQCll0+NaKvXfpULe8c7uSmx96iqdPJO0c6MGsaBXZ/oZ1oi/2yrSYZxBpFEiAnSJHdwmfPr+DGX75FzbrXWbmljpvmTuGJ1/az7PKp/OTlPaxbXEVxjjXqJ8VCuyU4B3lsrjWiJK/D7WXVwkrWbd3LzoZ2HnihnqduvYiy/Kzg3M5n7riE+xbM4oEX6nG4w+csSSlK4TOMAUYpBg9yR/oBS9MU5YXZFNgt2MwaBXYL5YXZ0vGnKMMw2H+8h3EFGZPDetjOmZDPrPH5/PSVj+hxeRPdHDFCxTlWpozNCV6vQ/vSdVv38uB1s4PX7VNd5xvbHNzz3C6+ffUswCDfZqHb5Y0Y6Hp0STVlUXIji9MnUywSQNcNmrtdLOuX1mXF5t388N/Oo7zQzrevPpvSXCse3QjOSw4EuxVFdsqL7DxzxyXYzBr/+fw7ESV5v3HV2cFpGOAfTf6wuRurSYs6tzN0zpKuGyilor6vzG3KHCaluHJWaUSxDk0N3gGPxgcsydOZPo53u+l1+xiXL3/PgSil+NJFE7nvd+/x6P/u42tSTCWl6bqBxeS/jhbYLVw5qzQsr7sBbLjtYkxKoSmiTn0MLMprbHOgKUV5oZ3WHjc3/vKtsAxDvW4fZQWS0m20SYAcJ6HzMTUFbq/Bg9fNDqbOAv9tloox2exv6eHP7xzl2jnlEenYnnhtP1+dP50elxdNKbpdXr599Sy+H5JQfP2SKmxm/yInODmq/Pf6Jm6eN4X1i6tYGlLCN3TOUrRKPGsXzcHp0SnKsVAUkmZGpLfcLI17r5pJ4wl/p201+R/nDqHcs9VsihpcyweszHSgtS+DhaR4O6WzSvO4eMoY1v9tH9dXT8ykqpFpxePxUd/cHRwEC9ztnVKczVsH2vnGVTPCioJsuO2iiOtyYK3R9VUV3DX/LJSChrbeYEq4wJTKQOrXLqcXn44sph9FEiDHQWjQGS0P8YPXzcZm0bjr6ZMpX5689SJu7LfKdcXm3fz6jkv47h/eCwbDgaD5P+ZP574Fs3B6dDZtP8Tn50zk93fNw+H2l5G2mhRl+TY+ONbN2Fwrj99yEWZNYTYpLH1zjotzrFFvjd+5cQcrrzmXwOKsyWNyMMch1ZdILJcHjne5whaIrq6ppCDLAoNct4vsFr46f3rEBUI+YGWmfS3dAIzLl4BvMIsuPoOdh3bzgz+9z8M3zEl0c8Qw6brBkU5nxB3iZRtq2XjbxSy+VPGlR98Iq3dwtN0ZzBhUnGOlJM/G028cYFppLrddNoXDfVkq+qeEAyJSv8pi+tEjUU4chAadyy6fGpGe5Z7ndtHW4wnbdqLHHfUW9bEOZ/A2TSBoXlg1kaUbaqk72sUtj7/NZTPKeOgv9Xh1g/KibCYU2tE0RUtfsPPZh7dx82Nv0en08N0/vMfF//VKcBHVqUph3/3sLhpOODjS4RjyYiuRutw+PWoqIbdv8Ep6bQ5P1AtEm0OqhWWiD5u6sZo1SmXKzKBK8rL47OwJbNl9lK31zYP/gEgqx3tctHS5ol5HW7pcEWs7AjHBS3XNLH2qlpp1r7Po52+y+NIpfPWT0zjc5ozaD391/rSoi/tkMf3okQA5DkKDzoHyEGdbw28991/lCv6pEv0P/NCJ/IF/A0Fzr8uH16vT0uWiy+nFrGn88N/OCy7Oe/iVPRGlK5VSUd+33eEJtrO5yyUnYAbw6dEX6fmG8OFIFnmKUB82dVHR90FdDO5zsydQXmjnP3/zDt2yYC9l6LpBr8t3yuu3YRhhzw0UE+iGgcenk201RX1+akkOM8flST8bQxIgx0Fo/fV2hyfqidPbL4vE5toG1i2uClul+rMb5rC5tgHw501cv6SKTcsuZUyOlStnlYZN6C/OsdLS5aK+2Z9q62u//ideXeebv3mHLzzyBiu31PHV+dM5tzyfv9x9GddXVdDY5sCk/HkU+2fFWLd1b7CdgbnUIr1ZNP8ivfVLqnjmjktYv6SKK2eVYhlCkBN6zAfIIs/M9cGxrojjQQzMata447IzOdrh5Lt/eC/RzRFD1Nrj5link821Dfzshjlh19GHb7iAArsF3YCNt13MlbNKAYJ1CkJVFNnx+AxMmjbg82aTRrbVLP1sDCljKElNk1h1dbWxffv2RDfjlE5nDnJgUd2SuVM41uGktcfNy3VNXHNBeUTS8UARhb990Mz08fkU51gpy8/ieLeTr/zqn8FqfSu31EWskr1vwSxWbqljzaI57DjQypXnjAcFGOD2+VMzBYqXrK6pxG41sebVj/jOZ89J1ep6CWtsKhyroTodTpq6PDSe8N85CFR3KsuzkG8/9WKraIs9ZW7csKXFsdre6+b87/6FRRefwYLKCaPympnimbcb+O0/D/PTL13AZ2cn9e8uLY7VkTrc1svxbhdKKf646zBfuGgSJ3rceHw6Zk1x97O7KMm18dX505g8NhtNKdp73Tg9erDeQeB6vuH1g9zx8TMx8K8FCY0Z1i+uCval0s8O25B/MbJILw40TTGjLI/nl8/D7fVht5p4bumlHG530Nrj5od/9peTfPyWi2jvddPa4w6maPvUOeOpWfd68LX2NHfzQE0ltzz+dti8o4df2cOXPzGNLz99svLY2kVzgmWpB7qNE9j+8Ct7+I/507n+kTeCP//Lm6spzbPx4y+ej24YHOtwsubVj7hl3hTuenonLd0uORnTmNNjDLBIz8xga636H/Mp+mFKjIIPm/wL9GQEefgWVpVTd7SDFZt3c/b4PM4qzUt0k8QpZFk1NKX4ad/0RY9PZ1xBFl6fweJfvBmsrNe/Ounvdh4OpmwrL7Tzk5f38Nq+1v+fvXePj6q89v/fe88tkwtJCOGaIHc0xSBEkYv1Rou2peVYQFsBBS+AVD21Xs85pfUc6u+I1OOpVQRtK4p4QZCvFo+XVkVbEJWAoEYjImAikISQe+a+9++PyQwzmZlkcpnMTLLer9e8CHv2nv1Msvaz117PWp/FLy4dQ1aqkcwUE88vneqVjjOq5KZZ/IXyMs/Gjh5PsVAUxaQoyl9bfk5RFGW7oij7FUXZqHgJ2dbTY4wFPk3XYdmp9E+zMKhfCmkWI6u2l7CvrJaqRgdGVWHeuvdZtrHYr1/cOpdpX1ktdTZXiLM7tyjf7xzDafWJW2eOBSKndvjSMnyFfoHHX7dhD+W1Nn75/Md8WdGIyaBy1+Vn8cDrp9tcS0FA78Xp0SMU6UW36hRo874OjULfo7SiAYD87NQ4jyT5MKoq/zpzHCaDyo1PF1PXLEWuiYzTpXPTpr28WVLJuh2HOFLdTJ3NhdLSGjpcUd3dWw8ws2CQvwvukeomdn1dzfqFRQzLtJJptTCwXwp52akMz0ljSKY1SEVK5tnY0aMRZEVRrMAHgE8BfSFQruv6bEVRtgPfB4aH2fZmT46zJ1BVhbG56WxeNg23R8NoUDGqSpBY+KT8LDKtJjZeP4WKege6rmMyqAzulxIiKh6pE8/o3DTevfNiDKrC5mVTufeVz6hqcHLrzLEMz0nleK2NK4vyGJ2bHvH4e35wJrU2F/e9+jn3/OBMv/Pu26d1PnKg5nPgE22k7UJiEqlITxRMhI5QeqKeVLOB/mnSJrwz9E8z88uZY7nv/z7npk3FPHXdFEwGKR+KF5qmc7LRgc3lwaAqmFUFDXC6veo+uekW1swrJN1i5I9vH+SaaSPol+LtfNvWSq4vtUIBVs2ZwOAsi8ipxpkedZB1XbcBhYqifNWy6VJga8vPbwOXAGeE2dbrHGRN0zlY1RiUO/T0dVN44ppzg3KVA8XE18wr5L5XP2fKiCweW1jETQEaszlp5rCdeA5VNbFkw0f+4++6/EwAFj/5UVC+U01LpLr18WWnbEHHt+6iFq4LX7icqLG56SHfV9IzEhuLUQ1rE2aZtIUOcKC8jhE5afSSxcC4cOaQftzw3VGse/cQd289wIPzJ8rvMw5omk7piQZu3Lgn6L6cajawafc33DJzDHddPh67S+PJnQe5/oJR3P7ifr/T7Cu4az2nDsuysmrOBH778mf+1MVsq0gixpt43+lygLqWn+uB/hG2BaEoylJFUfYoirKnqqqqRwba3YRryHHNXz5kUD8L21bM4JGrJ4Vd3l5+8Wgmj8jhj299ycrZBbx9+0WsnF1Avd0V0pt9zbxCHn7rYNDxZadsGFSFlbMLmJSfRXmNjRWb9uJwe1g9N/LxuekW7C6NvOwU/nHXJbxzx0X8/VcX8dyN5/u78EX6Xg/9rZSKBjtNDnfQeW98eg8nmxw98wuPE8lsqwrwh5+dE2QTf/jZOfGrxhFiSixs1eH2UHKsntG5ad3yeX2Zi8blMq8oj5f2fssDb5TGezhxJV7zanWT0+8cw+n7anaamVtnjsWje1uGD0g3M7co319452PsoLQQdaonrjmXwf1SmDAsk0eunsS2FTMkcJQgxLtI7ySQ2fJzZsv/08NsC0LX9ceBx8FbwRr7YXY/kXRibU4Pw7JT+bamOeJSDMCbJZW8WVLpV6d4cP5EHni91J/oPzDDwq827w9Jh0g1G6isd7Bqe4m/leW+slpMBpX7X/vCf/zQLCu3PrePfWW1TMrP4o7LxodVz1i3sIgh/XT/xdz6e03Kz+La6SO5KqD4L/C8zQ4PWpreayeDZLZVj65jUBVWzZngV7EwqApakivfCOGJha2WHKvHrelSXNZN/HTSMGqbnTy24xA5aWZu+O6oeA8pLsRrXs6WLg8AACAASURBVHWEuW/npluoa3Zx06a9Qauyvkix7/7pC3jNKhjIphvO51STk4EZFoZkWv15xEJiEe8I8lvArJafLwXeibCt19GeTmyk92ttrqCCu3U7DrF6rnfppqrRwbKNxVz1+G6+rGykqtERcnyz0+Nv+nH3Vm9E2ve5vt7ut7+4H4db8x9/1+XjMagK//7DApxujdx074Xs645W0WDnVJMDt1tDURS2LJ/G+kVF/h7x4YoSfOc9fLKJWpuTqgYH39Y0U9XgkBzXBEHXYe07X/k75zk9Gmvf+Qr58wjRsr/lAV0iyN2DoigsmT6S80f253evfs6Le8riPaQ+hUKoGsu///AsTjY6eXD+RNYvKiI33cKKTXv9ecet74FvllSy4E8fUNvswmw09NrgUG8g3g7yJmCYoigHgFN4neNw23odOWnmkIYcT1xzrj9dIdz7a+Z5G3ZsLS7jsZZlmn1ltTy16zBjBqbx2ILTwuRbi8uC/u87PjvNxFslFaxfVMSD8ycyblA6f1l8rr8BiS8qbDIqrJlXyKyCgaSnmLjjxf1c/PsdrHz5U+64bDyT8rMAr8PrcGuUnmjgyKkm7n3lU+ate59V20u447LxDO6XEjYSnpNmZvXcQl775DjHa+1csXYnM1a/4295LU5y/FEVWHHJGMwtBUFmg8qKS8Yg87kQLR+X1dI/zUxOukTHugtVVfjFJWMozMvk7q0HeP3TE/EeUp9BUfBHhwFmFQzEajbw3IdHqbW5yEkz8+CVE5k+KodGh5vHFkyOWEA/ckBaUHqikHhIo5A40lrVIdtqosbmCvp/ZaODY7U27C4P+f1TqWpp86yic9bQTDyaV9nCoHiXxHUd3JrO8To7o3LTcLo1PJruXxp/ee+3TB7RPyhN4n+vOgeXR0NVFJqdHnLSzQzKsPg/z5ce4cPXYGTZxmLysq08ufg8lmz4iFVzJuD0aCzbWOzf74WlU8Me/+Ti87irpZ+8T2c38P1tK2bEasmpTwnad0U5pKLOxtcnm4IE6tfMK2TUgDQGZYqmbQ+Q9LZ60Zp3yE23cPus8d0wKiEQu8vD//d/n3OkuomnrpvC9NED4jmcpLfVaDhWa6Om2UlNk5PBmSlYTQb+86+fhW3cNTDDQkW9nUyrmYV//iDkHvfSiukMzAhuuCRKTz1C1L/QeEeQ+zSB+oU5aWYOVjUGRVIPVjUyMN1CmsXIPS99wi+f/xiXR2PV9hJu3LiXn7U4nve+8ilVjU48ms5Vj+/ml89/jK7r2F0eyk41s+BPH3DB6ne4+okP+NHEoSEpD7984WPq7W5uf3E/qWYDmq6z8uVPOXyymZONzjZlaR5bUMQT733tz2/25Uj79jOqSthI+F1bDlDV6GDkgDTpJR8jfIoinY3Ou7TwOsguie4LUfBtrY2j1c2cNaRfvIfSK0kxGbjrsjMZ3C+FG5/ewyflde0fJHQYTdP9KYBGFdJb7sff+5/3ONXsYm5Rfsg91Vv4rtHs9KAoOg9dOTFktXhAmiXkPF2Zr4XuJ95FekIL4dQfbnx6j7+itXUXvsoGB6lmA3aXh7lF+Tz81pf87oqzg/ZzuLUQB+dUU3iHd/zgDFbOLkDTdW5+dh8rZxdw99YDrJxdEFaWZmA/C88vncq24nI2F5f785t9+aq+/VRVDRq/yejVe37k6kmYjQZ09AhSYtJLvqu0ZVPRROdFB1noCv886FUXKMzLbGdPobOkpxi55wdnce9fP+PaJz9k603TGTlA8r27i9aypU8uPo/dh6pYt7CI5c8Uc6zWFjGFwuXx3n83L53K6IHpvLRiOi63FjEy3NX5Wuh+xEHuIdpbOomkaqFpWtBxWVYzx5025jy6M+Qcv/2xxrCAblXhlDCqI+gdn6izs2xjMVuWTwtqQb1uxyEenD8xqE/8mnmFeDSd5z84zPp/HPFvy82w8MDrX/g/05dTHbZCN+3078Wn/Ryojyy5WV3H6fYwfVQON144CoOq4NF0nnjv66ij8yaDEtZWjAZZ8hPa570vT9I/zcywLEnHiSX908z82+Vncu9fP2PRnz8Iu3QvdI7WTmuq2cD6fxwh3WLiuRunAjqaTsR7anmNDQ3o3xIt9vkBx+tsIX5AJB9AVlPjhzjIPUCk5hmBWoc+1YrAC2RWwUBONjlZtrE46LhB/SztRl01TUdRQh2crcVlPHr1ZH9bal9RXl52CjvvvsR/jE8pY19ZLfe/9gX3//RshmRasZhUUs0G+llMXH/haBZMG4lBAWvLtvuuKOS3P44+f0pVFeklHyPSLAYWTjuDJRuCm8KkWaKLzhsUhT/87Bz+9fmP/cf/4WfnYJAGBUI7eDSdf351knPys6ShRQ8wJMvKnZedye9eLWHxXz7ihWVTyUgxtX+g0CatndZam4tZBQOZMiqHnz/hTXF89ZYLWLtgMisCZN4enD+R+1/7grxsK5qm+1fd2vIDwvkAspoaXyQHuQeItHRS3eT07xNOteLXPyrwO8eBx7lboq6RFDB8Dvm9r3wa0vxjyYyRbNp9lFVzJvDW7Rdx/0/P5uG3vsTlgWHZqQzul8IT13hVLXzH7iur5Z6XPsHp0RiaaaV/mrcF5sCMFIb3T2VYdqp/W2d6wksv+djQ7NT8kzaczo1rdmrtHOnFaFDISDGyas4EXlg6lVVzJpCRYpQIstAu+76poc7mkvSKHmTMwHR++b2xlFY0sHxjsb/1sdB5WsutrttxiH//YUFQA5Bf/79P0XWd38+fyFu3X8SqORO4/7UvqGp0sHpuIb97tYTqJme7fkB7ylZCzyMR5B4gmqWTcJHUSMe53FqbUdfAC7GqwcnK2QXkpJkZnJkC6MwtyqPW5uKOgEYiv/2xJ2gc911RiKZpbF42DV3XJbKbhLg8Wlj7cXuiu3E2Ojz86b3DISkaN186hpz0WIxY6C1s2/ctZqPKpPzseA+lT3FOfjZLvzuKx949xK82f8wffjYJg8zZncbntPrvp40OFIWgeXVfWS33vlLCH6+ehKp4Hdvf/LiAygaHvyGW7/7alh8gq6mJhzjIPUC0Syetc3WrGhwRj2ur806gY+1r/gHw8i9mkGo2hLS/bD0W6erTOzAZ1Ag5xNEtHBlUhV1fV7O5uDzo+Fu/N7bbxyr0HhxuD9sPHOe8M7KxmmV5uKe5cFwu9XYXmz74hqxUE6vmTJA0l04S6LTaXB4OVTaih8k5rmp0oAAmg4ElG0JlTX331/b8ALn3JhaSYtEDdHbppLPHRerCl51qYs0bX4SkXcgyTu9kYLqlJb/89N963cIiBkbZtMFqVlkzL9hW1swrxGqWaUOIzDtfVFJnc3HB2Nx4D6XPMrtwKD8uHMIzu7/h/te+INn7HcQTn9Oal2VlcGYKVQ2OCPOioc17tqRQJB/SKKSH6KwAeGeOi1QUODY3nRqbC03T8Oj01dSJPiFo78Pt1qhsdOD2aBgNKgPTvbni0aBpOkeqmzha3Uyq2UCz08MZOamMyEnrS/YST5LOVnVd56drd1FW08z/XiXL+/FE13We3HWEv5VUcNPFo7nrsvGxjCQnna12Bk3TqbU5OdXkpOyULey82NY9WxqBJARR/8IlxaKH6OzSSWeOayuXSZZv+hZGo8rQTspsqarCiJw0MlJMMqELUfHewZPsK6vl+gtGinMcZxRFYfH0EWiazmM7DmFzevjN7AK5fruAqir0T7OQZTWTaTWHnRfbus/KPTi5EAe5lyIXotAdiB0J0eJ0a6x+7QsGpJu5eJykVyQCqqJw3QUjsRhVNuw6QmWDnd/Pn0iqWW79XUHmxb6BXCWCIAhCl/nfv39JyfF6fvW9cVEXggqxR1UUFk49g+w0M899+A2Hqpr4488nMW5QRryHJggJjcxigiAIQpd49oNvWLvjEJeMz+W8kf3jPRyhFYqiMLtwKHdddibH62zMfvif/O/fv8Tuki5tghAJcZAFQRCETtHocHPvK5/x79s+YVJ+Founj4z3kIQ2mJifxeqfFlJ0Rjb/+/eDXLD6bR595ysq6u3xHpogJBySYiEIgiBERbPTzbFaO6UnGth56CSvfHyMJoebWQWDWDT1DEmtSAKyUs3cOnMsswoG8dK+b1nzRim/f6OUwrxMzh+Vw3eG9uOMnDSGZKaQnWrGHKXqjSD0NsRBFgRBENpl/rpdfHSkxv9/q8nAuSOymV04hDEDpbVislGYn0lhfibHam3s/KqafWW1PLnzMC5PsPSrxahiNRmwmFRevfW7DIhSR10Qkp2k10FWFKUKOBrvcXSRAcDJeA+iG0iG73FS1/XL43HiGNtqIv/uZWwdZwDwRZLZaqL+LsORTGOFxB9vb51XO0qi/52ipTd/j6htNekd5N6Aoih7dF0/N97j6Cq95XskI4n8u5exdZxEHVdbJNOYk2mskHzj7av0lr+TfA8vklwkCIIgCIIgCAGIgywIgiAIgiAIAYiDnBg8Hu8BdBO95XskI4n8u5exdZxEHVdbJNOYk2mskHzj7av0lr+TfA8kB1kQBEEQBEEQgpAIsiAIgiAIgiAEIA6yIAiCIAiCIAQgDrIgCIIgCIIgBCAOsiAIgiAIgiAEIA6yIAiCIAiCIAQgDrIgCIIgCIIgBCAOsiAIgiAIgiAEIA6yIAiCIAiCIAQgDrIgCIIgCIIgBCAOsiAIgiAIgiAEIA6yIAiCIAiCIAQgDrIgCIIgCIIgBCAOsiAIgiAIgiAEkPQO8uWXX64D8pJXtK+4IbYqrw6+4obYqrw6+IobYqvy6uArapLeQT558mS8hyAIUSG2KiQLYqtCsiC2KsSKpHeQBUEQBEEQBKE7EQdZEARBEARBEAIQB1kQBEEQBEEQAhAHWRAEQRAEQRACMMZ7AIIgCIIgCL2RnV+d5PmPyhiamcKKi8eQmWqK95CEKBEHuZNomk51kxOn24PZaCAnzYyqKvEeliAIXUSubSFREFtMbt7+ooLrNuwh3WKkyeHmlf3HeOXmC8jNsMR7aEIUiIPcCTRNp7SigRuf3kN5jY28bCtPXHMu4wdlyOQlCEmMXNtCoiC2mNw0Odz8etun5GVbue9fzuZIdRP3vfo5tzy7l2duOB+jQTJcEx35C3WC6ianf9ICKK+xcePTe6hucsZ5ZIIgdAW5toVEQWwxuXlm91GO1dm54YJRmI0q4wZlcN0FI9h9+BTPfVQW7+EJUSAOcidwuj3+SctHeY0Np9sTpxEJgtAdyLUtJApii8nNK/uPMWZgOuMHZ/i3XTg2l4IhGfzPm6XU211xHJ0QDeIgdwKz0UBetjVoW162FbPREKcRCYLQHci1LSQKYovJy+GTTXx2rJ5po3KCtiuKwsKpI6hpdvH4u1/HaXRCtMTEQVYU5XJFUcoVRflny2uioijbFUXZryjKRsVLSjTbYjG+jqJpOlUNDr6taaaqwUG21cQT15zrn7x8uWE5aeY4j1QQhK6Qk2aWa1tICMQWk5ft+48BcP7I/iHvjRyQxpSR/Xnq/SMSRU5wYlmk95iu6/cBKIpyA1Cu6/psRVG2A98Hhke57c0YjrFdIhVKjM1NZ9uKGVJdLAi9DItRZdWcCaSaDTQ7PViMstAmxAexxeTk7S8qGTswnZz08GoVcyYO5cPDp3hm91FWXDymh0cnREssHeS5iqLMAcoAJ7ClZfvbwCXAGcDWKLbF1UEOLJS4/XtjmTM5D03XqWiwMzDdgskkci2C0FuobnLyXmkFlxYMQdN1VEXh7ZLjZEzME2kmIeYEyropisL9r33OmyWV/vfzsq1sWzGDnDRzWPk3kYWLP3aXh0++reOHZw+JuM+o3HQm5mXyl38e5voLRmKRtJmEJFYO8iFgpa7rryqKsgsoAv7c8l49MB7IAeqi2BaCoihLgaUAw4cPj8X4/fgKJW7/3lguPmsQVz+x2x9JfmxhEWcOTMdkEuMWwtOTtip0HYOqUzRyQMh1blD1eA8t5oitxpdwq5Wr5xZS1eBkX1kt4C3S0zQt4qrmwarGPiELl8i2+sm3dbg1nbGD0tvc74dnD+G/X/uCVw8c56eT83podEJHiNV6zSng7y0/HwE0ILPl/5nAyZZXNNtC0HX9cV3Xz9V1/dzc3NxuH3wgvkKJOZPzuOmZ4iDJnZueKaay0RHT8wvJTU/aqtB1bE4t7HVuc2pxHlnsEVuNL+Fk3e7eeoAH5hXywtKprF9UxKyCgXh0wsq/VTY6+owsXCLbavHRGgDGDcxoc7+zh2UyLNvKn/95GF3v/Q/gyUisHORfAT9TFEUFJgC3A7Na3rsUeAd4K8ptccVXKKHreljJHbcmhi0IvQW3Jte5EB8iybrV2Vxc9fhuVm0v4daZ4zAohN3P5dFEFi4B2HOkhiGZKfSztt1SWlEULv/OYD47Vs/eb2p6aHRCR4iVg/wIsAT4ANiGN71imKIoB/BGl98CNkW5La6oqsL4QRkYVSWs5I6xly1dCUJfRq5zIV5EknXzRYDLa2wsf6YYj07Y/UwGVWTh4oyu6xQfPcW4QW1Hj31cMGYAqWYDG98/GuORCZ0hJg6yruvHdV2/WNf183Rd/62u6w5d12frul6o6/oi3UtU22Ixvo6iqgoWo8raBZODJHfWLpgsVcWC0IuQ61yIF+Fk3VbPLWTdjkP+fcprbOi6Hlb+bWC6RWTh4kxFvYOaZhejBqRFtX+KycB3x+by6ifHOSnpmglHLFUsehU6Cq/u/5YnF5+HQVXwaDpb9nzDDReKRIsg9BbkOhfihW+10icfqigK977yqb9AD05HhMcPsoaVGQ08XlQsep7SigYA8vunRn3M9wsG8cZnJ9i8p0wk3xIMcZCjJCfNzL9MzmfJho+CKoTl6VwQeg9ynQvxRFUVv5ygpunc9v3xlBxvCLHFwP0iHS/0PF+e8DrIrVNd2mJYlpWzhmTw/IdlLL9wtDzQJBDiIEdJ66dzq9mAW9M5XmeTJ3VB6CWoqsLY3HQ2L5uGy6NhMqgMTLfItS30ONFGhEX7OHEorWggO9VERkrbBXqtufTMQTz6zlfs/rqa6WMGxGh0QkcRB7kD+J7OI3XX6416k4LQl9A0vc9oyQrxoSMObXsRYbkXJRalJxrIy44+vcLHlBH9ecpi5NkPvxEHOYGQypNOcLIpvN7kySZJsheEZEaubSGW+BzaK9buZMbqd7hi7U5KKxrQOikjGE47ubdqHyc6mqZzsLKB/A6kV/gwG1VmjBnAm59VUNssf7tEQSLIncDu8pCbbmHl7AKGZqaQYjLQ6HDjdGv+iU6WvAQh+bC7PFxVlOdvKa8qCi/vLcfu6v2NQoTYU93k5KG/lbJydgEDMyykW4w4PRon6u0M7pfS4fSJSNrJon3c85TVNGN3aeR1oEAvkIvH5/LGZyd4Zf8xrpk2onsHJ3QKcZCjoPUklWJQuevy8Ty58zDXTh/JTZv2+pe3nr5uCg63JktegpCEpFsMYVvKp1tksU2ITLRpE5qmce30kdy99UBQO+k//P1Lbvv++KD7RFvpE4A/SpyXbQ1ykkX7OD58WdEIQF5WxyPIACNy0hg5II0XPioTBzlBkFm/HcItiTk8OnduOcDconz/RAfeJ/ej1c2y5CUISUpfbjUtdI6OpE14dELuGXdv9d5LWt8nIqVPnGxy+M9387P7WDOvULSPE4Cj1U0ADMnsnIMMcOHYAXx2rJ7SFjUMIb6Ig9wO4Sapino75TU2sqymkOWtVLNBlrwEIUmRVtNCR+lIHrCuh7cv370k8D4RKX3C7jq9QrmvrJYHXi9l1ZwJvHfXJWxbMUNWK+PEkeom0i1G0lM6vzA/bfQADKrCS/vKu3FkQmcRB7kdwk1S1U1O8rKt1NpcIXqHzU6PtPsUhCRFWk0LHaUjecCR2kn77iWB94lI+xoUgs63r6yWJRs+wqBAboZIEsaLo9XNDO7XNQ3qTKuJiXmZbNv7LR55KI874iC3Q7hJamtxGesXFbG1uIzVc4OXt/qnmVi/qEiWvAQhCUk1qzy2MPj6fWxhEalmmSqF8ERyZMMFRSK1k95aXBZynwi37xPXnIvVHP35hJ7j8MkmBvVL6fLnXDAml8oGB+8fqu6GUQldQdH15H5KOffcc/U9e/bE7PMjFUqMGZBGVZMTl0dDVRSaHC6O1zk4IyeV/CwrVU1O3B4NY0ujAaNRbrAJQtzCK7G2VaHruN0adQ4ndqeGW9MxqgopZpVMizke17DYahIQ7h7x9HVTSE8x4nJrIUV7brdGZaMDl0fDqCpYjCo6StRNQIBE1D7u07bqdGucufI1/mXSMOYX5Xf5s5Y/U8yPJw7hgXkTu2mEQgBR26qoWLRDuG5G2VZTSDOB9QuLmJifST9L6HsJMHkJghAlx2sdLG8p1MvLtrJuYRGZg2QFSAhPuC6rFfUOrlm7K6z6RFfvD9F22BN6jvKaZjQdBndDBNlsVDlvRDavfXqC/5ozgRSTrAzECwlrRoGvm5GvOrWywR5SlLHsmWIcLm9kQFQsBCE5qWw87RyD9/pd/kwxlY3SKESIjO8eMSw7FY9GxHtARwr62lLHCDyf5B3Hn6PVzQDdkmIBMGPMABrsbnaUVnbL5wmdQyLIURK4jLZhyXn+RiFZVhO1NhfrdhzC5vJwqskpKhaCkKS4PFrYa9vlEZk3ITraKtrTIer7Q2BTEZ8tPvS3Uu67ojBi++mOtLEWuo8jLRJv3RFBBvjO0EwyrSb+uv84l08Y0i2fKXQccZCjQNN0TtTbaXK4WTm7gFSzgbsuH8+dW06LvT+2YDJGg4LLo4lwuyAkKSlGlX//4Znctnm//9p+6MqJpEgNgRAliqKEvQcoigK6Hva9cGiaxvUXjOL2F0/b4oPzJ6Jp4R/W2mosIk5ybDla3YzVZCCjCxJvgRhUhfNH9uetzytodLhJt4irFg9k1o+ApulUNTioqLPx+Yl6rlz/PvPWvc+q7SW4Nd3vHENLM4FNezlU2YSqKDxy9aSgyuP1C4twax4qG+xhxeMFQUgMNB2/cwzea/u2zfuRy1aIBk3TMRpg7YLJQfeAtQsmYzYoKAohyker5xYSzn/VdPzOMXht8fYXI9tiR9I3hO6lvKaZgf0s3oegbmLa6Bzsbo23Pq/ots8UOoY8loQh8El85ewCVm0vCZp0KusdYZfJUs0Gbn9xP/f/9GxWzi4gJ83M4MwUNu46zPp/HJEnekFIcJweLey1LSkWQnv47hsmg8ojbx8MSo145O2D3PuTCSiKwlO7Dge999Suw/znnAkhn+fSItiipoVNpeiIHrPQvZTX2MhJ65oGcmvGDcogJ83Myx8fY845w7r1s4XokAhyGAKfxMN1y/M1CgnEJ/ZeXmPDZFBZtb2EFJOBv392nAvHD2JSfpY80QtCgmOI0ChEHmiF9vDdNxrsLt4sqWTZxmKuenw3yzYW82ZJJbquYzYo3HzpWFZtL+Gqx3ezansJN186FrMh1L4MSnhbNChKUPHef2w7QHlNMzqh6Rr+1A4hppTX2CLmhXcWVVGYOiqH976soq7Z1a2fLUSHOMhhCHwSD9ctb2txGetbNRNYPbeQdTsOkZdtZWiWlScXn8dfPy5n0hk5PLXrMMsvHg3IE70gJDImVWHNvOAl8DXzCjGJgyy0gdut4XR7ePq6KeSkW5hVMDDofV8dis2l+aPLLyydysrZBTzy9kFsrtAVihSzGt4WDao/gDMpP4trp4/k6j99wC3P7gvZf/XcQsL43kI3Umdz0ehwMyC9+6Ugp43Owa3pvPHZiW7/bKF9JMUiDL7OSOU1NtbtOMTquYXcvfV0Qd5t3x9PVqqRjddPAeDIyWZ+/0YpVY0O1swr5Nbn9lHV6GD13EIefecgc4vyybKaACnYE4RExmJSGJBhYdWcCaSaDTQ7PQzIsGAxiZchhMft1viioiFEO3tkTmpQal1OmpmymmaqGoJXEKsanGFrUxQUUs2GIFtMNRvQdd0fwFl+8Wj/vam8xsYDr5eyas4E8vtbOVTVxFO7DnPfFYU98nvoq3zb8rfITe/eCDLAqAFpDO5n4ZX9x7jyvK41IBE6jkSQwxDY4nNfWS1P7TrMszecz867L2HbihkMybRw5GQzi/78Ib96YT8A//uzc1g1ZwIPvF7KvrJaymts3L31AHOL8slJM/sj0dJ2WhASl2anzprXv8DZknPs9Gisef0Lmp1SpSeEJ5J29tVTR7DrnkvYvGwaY3PTUVWFdItXASkwxeKuy8eTZgkNmticHu59pSTIFu99pQSHR/dHiVunAO4rq2XJho+obnSyansJt31/vNxvYkx5jVcDeUA3p1iAVxFl6qgB7Dp0kqoG0WLvacRBDkNgp6Kdd1/CfVcUkped6hdlb3J4/CoWvgmpqsHBkg0fsa+slkn5WaxfVMSD8ycyblA6Q7NSKMzL5KUV06VATxASGJdHCxvhc0uRnhABV4TCzqoGB59861VAOljViKbpeHRCFJDu3HIAT5jnL7PRQG5GsHObm2EmxaT6AzjhUgDzsq3kZVvZtmKG3G96gG9rYxdBBpg+OgdNh1cPHIvJ5wuRkRSLCPg6FYXDE7DE5cNXuJebbuGOy8YHpWQ8sehcxg+WiUoQEh2LUQ3ROF8zrxCz6CALETAZ1LDaxtVNTn+E98an97S0ho6gTOEOfQDLtpq4dea4kNSN/lYzA9IsbFsxA03TWL+oiGUbT+/zxDXnMiRTCkt7ivIaGxaj2m0ayK3J75/KGTmp/L+Pj7F4xsiYnEMIj8z6nSDFZAhbuPfo1ZO5deZYv3MMLVqUG/dwskmWRwQh0dHCaJzfueWA6JcLERmYbmFdmKLtrcVl1Nq86gO+4myF8EoT4aixucKmbtTYXP4AzqBMK2cN7udf7ZSocc/zbYuCRSzVQmaMHsDHZbUcOdkUs3MIoYiD3AkGpFn8S1zgneCunT6STbuPkt/fGjZCYA9TpSwIQmLh1kJXh8prBKghbwAAIABJREFUbLjFQRYiYDSqjMtNY9MN57Nl+TRWzi7gqV2HuXb6SNbtOASclltTO9AoJFpdY5+z7EsBFOe4ZymvbY55nvf00TkowEt7y2N6HiEYSbHoBL4c5ReXT8Pp1jCoCv/11894s6SSmQWDwi63GWXOEoSEx6eD3Pr6NYjTIURA03QOVTfz0N9KmVuUz4icVO79yXf45pSNe35wJs1OD3n9raSYFGxOPWyjkN/++DshnxuopuRDVJASj29rbEwenh3Tc+SkWyjMy+TF4nL+9XvjZD7qISSC3ElUVcFsVKist/Nff/2Ma6eP9OaI7TgUokW5fmERBlXh25pmqhocslwrCAlKikkNWS5ft7CIFJNMlUJ4fA1CfM1BUkwGapqc2JynI71OlweXW0dV4foLRgWpWFx/wSjUMOYVqKYEiApSAmJ3eahpdpETowK9QC4aN5DjdXZ2HToZ83MJXiSC3AXsTo3bNu9vqVh2+ttL52ZY+P38ieRmWLA7PVhMKnPXvR9URCF5YoKQeHg8Ogp6kPasgo4nnMyA0CcI19Y5cO5unQphNqrU292sfPnToELPnHQLKgopJjXIvlJMKiqh94JANaVI5xbiy4k6OwD9e+Ch5dwR2WRYjDz/YRnfHZsb8/MJ4iB3icB8xX1ltSzbWAzAC0un8rPHdzOrYCD/8aMCFvzpg+CivZaK5u5uTSkIQtdwajrLntkbsqz9/NKpcRyVEC80Tae0osHfuS5cgKN1KoTHE77Q8/mlUzEoCr94dl+Ifb24bFrY87elpiTEn2N13r9jT0T1TQaVC8fl8vqnJzheZ2NIZvjiTqH7iOm6oaIotymK8ndFUQYoivIPRVE+URTl/pb3otqWqGiajlFV2LJ8GusXFTEpPwvAr00JMLcon6oGR1SFFoIgxB+PppObbmH9oiJeWDqV9YuKyE23SFpUH8WXPtE6wFHddForu3UqRCRdZE3T0fQI9qWLfSUjvghyTgzaTIdjVsEgNF1n0+5veuR8fZ2YRZAVRTkDWAxUAb8EXgUeAPYpivIX4Jpotum6/mWsxthZwkUVVs8t5Kldh1lxyRga7W5eWDqVgf0snKizS6GFICQJKUaVe39SwKkm70Ou2eD9v0V0kPsk0ShJ+FIhXrl5BjanB7em8+Ti83j4rYPsK6sFvHO+yaBiMYXX2baa5X6QjBzvwRQLgIH9Uph8RjabPjjKiktGk2qWJIBYEstZ/w/Av7X8fCnwN13XNeBd4JIObEs4wkUV7t56gP+cMwGXW+Oelz7hqsd3s+jPH2JUFR65elJw0d6iIim0EIQERFEUmp0eVr78KVc9vpuVL3/qzUOOocapkLj40icCiRTgqKh3cNXju7lozQ5Wvvwpd10+nkn5Wf5C7dx0C+4IOtsiI5icHKu1kZFixNKDAa+fTBxKTbOLZ3Yf7bFz9lVi4iArinI1sB8oadmUA9S1/FwP9O/AtnCfv1RRlD2Kouypqqrq/i/QDpGiCh5N9xft+bbdtnk/dpfGytkFbFk+jWdvOJ/xA6VAr68Qb1tNZjRNp6rB0aPqLy6PFtaBcfWBVtN93VbD2Vu0ShLhgiZ3bjnA/1w5kVVzJmA1G1BVBVcHOukJkUkUWz1RZ+/xYNe4QRkU5mWy7t2vaXK4e/TcfY1YxednA8OBy4DxgAZktryXCRwFTka5LQRd1x8HHgc499xze/zRW1HCa6VqEZoM5KZbUIAGu5usVFPU52mvelpIfOJtq8lKNMVRsSBSoxBPH4jw9WVbjWRvg/pZ6J9qYvOyaei6HnEejhQ0qWxwsGTDR+RlW9m2YgZmo4FZBQOZW5Tv10HeWlwmKXcdJFFs9Vitjf5pPV9EOW9yHr955TMeeecr7r78zB4/f18hJhFkXdev1nX9AuBnQDHwKDBLURQVuAh4B3grym0JhyFMN6QH50/0O86B5GVb+aqqkase382SDR+x+MmPOFZnazca5puwr1i7kxmr3+GKtTsprWiQYiGhT3CyyRG2OCrWLdvNBjXsNWwySA5ybyZSMd7+sjrO/++3uXL9+9Tb3RGDFKYIduNbefDlLWdbTdw6c1yQDvKtM8eRbY0+cCIkDsfq7D2WfxzI2EEZXDQulyfe+5qDFQ09fv6+Qk/N+g8DPwQOAK/quv5VB7YlHIqq+LshvbB0KitnF6AqCqu2fxbiOK9dMJl+KcagimW3pnOi3t6msxtN9bQg9FbsrvARuVi3bFcVQhr9rJkXvhWw0HuIFAFObSmea2v+1TQdt6aFtRtfxzNf3nKNzcXyZ4qD5vXlzxRT06J8JCQPNqeHOpurxxQsWnP1lOFYzQZ++cLH2F2iihULYloCqev6EeB7Lf/9bqv3TkazLRExqgq/uGRMUKX74EwLb5ZU+huGZFlNuDwauRkWnG6N6iYnW4vLuOvy8VTU27lzy4E2l4yjqZ4WhN6KQVHCLkUbYuyo2t0a2/Z+y5OLz8OgKng0nSfe+5pfXDomticWuo3OpKZFautcG+C4Rpp/q5ucVDU4eeD10qAW0g+8Xso9PzjT340x22qiosHuP8ek/CyWXzyaLKsJp9uDpumSQpdEHO9BDeRw9LOaWH7RaNa8UcpvXv6U1XMLpZi4mxGNkE7gcmvewruATklrF0xmVsFAf7vRSflZ3HX5eOYHdNB79OrJGA0KKSYDuemWNhuGRJqwJVdN6AukWQzcculYbtq013/9PLZgMmmW2Np/ilHlisnDWLLhoyAZrhSReUsKOpu7nm018ewN51PZ4PAHM5bMGMkDr5f694k0/zrdHlLNBqoaHf5mUb79h2ZZWTm7gIff+pL7rij0z+u56RbuuGw8d2890KM59kL34ddAjqMi1eTh2fx00jA27yknN8PCnZdJPnJ3IrN+J/DocPuLwWoVKzbt5T9+VOBfYrt15tiQavhfPOu92V/7lw+547Lx5KZbIkaEo62eFoTeiNOt+51j8F4/N23ai9Md2xx8jx5ehssjjRySgs6kpmmazsGqRq7+0wfMW/c+q7aX8K/fG0detpWqRm/Oe1vzr9lowOnRgtLrZhUM5OnrpuDWvClBVQ3eiLZvXr915li/c9zWOOOh5CJEx4l6r4OcHed78ryiPGaeOZBH3znE6te/QJe5qtuQCHIn0PTwle4KsGHJFFQFVEUJu0+W1UR5jY2ndh1mzfyJeHTvBNh6GdAnPr9txQxRsRD6HPYIKUb2GMthRVKxEJ3a5KAzqWnhnOplG4t5acV0tq2YgaZpeHTQdW/qRut5OCfNjM3p5g9//5KVswsYmpmC2ahytLqZVLPB32zGJ/U2flAGaRZDu+OMl5KLEB093SQkEoqicN0FIwF4bMchGu1u/vMn3xEb6QYkgtwJ1AhqFTqw+MkPufTBdzl8sinsPrU2F5Pys7h2+kgWP/khFz6wI6JChaoq5GZYGJadSm6GRQxe6DMYIlxjsc5BNqrh1QiMqkyVyUBHGnv4iORUu9waOWlmTjW7uHL9+22qCSkK3HzpWFZtL6HR4eZUkzOk2YyO9xhVVbCajO2OUwq1E5uKejvplp5tEhIJVVG4/oKRzC4cwsbdR7nnpQOy2tANyKzfCcLJvD169WTue7XEP5k9/NbBkKrm1XMLWbfjEMsvHh3V8pog9FVMBjWsKkCs5dZExSK56UxqWltOdTROanWTk58/8QHPvH+UJxefR37/1LBpOnand/VD03QMKqxfWNTmOKVQO7E5HieJt0goisLVU4b7c5JXvVrS/kFCm0iKRRS0roo2GVW/zJuvYtloUHizpNJ/zL6yWh54vZTnl06lqsFBptXE/a99zr6yWnLSzDLxCUIb6LqO1Wxg1ZwJpJoNNDs9WM2GmOfXOSKoWNwsKhZJQWdS03xOdetUhmyriWP1tnbnaqfbQ266hTmTvMWdG5acF77ZjO69lxypbuJodTMD0s1sWDIFk0HBajYwIC14lVAKtROb43U2sjvQ+KsnUBSFeUV5NLs8PLnzCOMGZfDzKcPjPaykRRzkdoiUB3bPD87imr986N/29HVTQiYzX4HHLc/tIzfdwu2zxrFydgGgsGX5NKqbnKzbcYh9ZbUy8QlCAIqq8NqBY8w7d7jfUd2y5xuuv3B0TM9rFRWLpMeXmtaR/ccMSOOFpVNxazpGVSE3zczBqkZO1NnbdVLNRkNQ0V2kY1JMKrU2JxX19iAFpDXzChk/ODSvOJLjLoXaicGJOgdnD8tsf8ceRlEUFp1/Bsdqbfzm5U85e1gmExJwnMmAzPrtEGmJLb2l+cdfb57Bk4vPo8np4enrpjCrYCBwuruezelm9dxCcjPM5PdP5USdnZ8/sdtfLX3HZeOZVTBQJj5BCMBihNnn5LFkw0dc+uC7LNnwEbPPycMS40d6lxZexcIl+Xy9Frdbo7TS2+30ojU7uOrx3ZRWNfH/9pbx8FsHQ9Lp1i0sQlV1f45nTpqZEQNS/Tbz4Jtf8uD8iSHpE/2tZmxOT1j7sjlDVw8Do+E7776EbStmSIFeguDyaFQ3OuifllgRZB+qqnDzJWNIsxi548X9OGNc3NxbkQhyO7RVwGE1G2g85Q7Sal27YDK/nl3A58cb+PM/v+bnU87gtU+Oc/us8aiKwm2bg+Xh7t56gA1LppBhMYStkBaEvkijXeOmVh3HbnqmmBeWTiXT2s7BXcATQcVCZN56L5WNjpDudjc9U8xT101h8ogc+qUYeXLxeTQ63GSlmvjVC/upanQEKUqkmb37pJoNLU1tylk1ZwKjc9Owmo1kW00crGqkyeGOmH4Rjo5Gw4WeobLBgQ70T0vcv01GionrLxjJg29+yePvHeLmS8fGe0hJh0SQ26GtAg53mGjTik170XVvd71bLh1LdpqJ+efmc6rJu7QWbnKsbnRwpLo5YoW0IPQ14iW3ZlAjqWfIQ2tvxeXRwtqaUVVYtb2EHz+ykyUbPsLu8nCizs6+stqgYj1N06luPK1asWp7CVdMHkZ+fytDM63kZliosbn8+4ezrxST91YsusfJwYmWLnqJGkH2ce4Z/Zkyoj+P7jhERYtusxA9UTvIiqIUhNl2QfcOJ/Foqyra5Q4/sZ6os/ulfZ7d/Q15/a3YXV7JoHCTY7PTQ63NFbWahUyiQjLQFTs1RnBUjTFeXVHCKNSsnisqFslMe3YYydYAnlx8Hm/ffhFPLj6PHV9U4Ak41lesV93k5MaNe0LSJvqlmDC25K77ViLX7TgUYl9PXHMuA9Is/nqXK9bubFNSTog/pzWQEzeC7OPq84fjcmv8/o3S9ncWgmgzxUJRlLMBG+AGHlQU5Ra8TnUjMB1YBnw/1oOMJ21VRUeqMvY5u7e/uJ+HrjyH6kYnz314lKUXjmbj9VM4crKZh986SFWjg7ULJmM2Kvzb1k+B9tUsRDxeSAa6aqepFpXHFhb50yzysq08trCIVEtsF700Hd4rrQhSsdiy5xvOyBkZ0/MKsSEaO0yzGHhsweSgVLl1C4twuj0s2XD6uLULJmM1nbY/30piYBrepPwsll88miyrCZdHQ9P0oHvFvrJafv9GKStnF5CTZmZolpXB/VJQVYWqBkfYepdtK2ZImkWC4Wsz3T818euGBvVLYVbBILbuLeemi0czKjc93kNKGtq72/wf8DZwA1AH3AH8HVgA/BKv89zridSwI1x02ad1DN4JbkhWCn/9uJx7fnAWAF9WNPLch0f5rznfYc28Qh55+yAnG5zsK6v1f0ZbahYiHi8kA12102aHxvaPy4MieNs/LqfZEdtiE6tR5UcThwUVB/5o4jCsomKRlERjh/1SzAzNSuG5G6ey486LefbGqQzqZ/E7x77jVmzaS4rJG1MKXEn0Ob+T8rO447LxrNpewlWP7/YW+7VEgAPvFfvKalm1vYQ0i9HvHIPoHicTJ+rsmI0qaZbkUJ768cShGA0qD791MN5DSSraK9L7CtCBz4FRwDvAeOAo4Irt0BKfwOiyzeXhUGUjv3+jlLED03lgXiEGVcGkKvz4nLwgSbjVcwv549sHmVuUz5sllSxtka6KRsZHJlEhGeiqnSoKXDh+UJDc2uq5hcQ6Fdjp0VnREkmE047Ri8umxfbEQkyI1g5P1DlYFrBasW5hEbnplqBjy2tsaLrOzrsvCVpJzEkz8/R1U1AUWPTnD0Oc8c3LpjG4X0q7+syie5w8nKj3NglRkqQ2ISvVzKyCQbyy/xi3zhwrUeQoiTYsogMKUAgMAb4HDG159Wl80eW8LCtn5KTyn3O+wzXTR/gjUCXHG0IqpO/eeoC5RflkWU3kZVvJSjXz7p0X88LSqYwZkBayBB2YQ6dEaMErk6iQSHSm5W8guk5It8m7tx4g1mISomKR3LTONzYZw7cOD7TDk02nnWPw/r2XP1PMrTPHhhxnVBUGZaQErST6z60T1naO1doorWgACLsS6aMzXQCF+HCizp4U6RWB/OjsIRhVlfXvfh3voSQN0TrIviu5EXACpwBHy0toweHSqG50BjnEqWZD2EkzJ81Ms9PDQ1dOxKN5KDvVzPE6O8fq7bhcp6MbrQs37n3lU9a106JUEOJNV2/2mh7eUdVi7KgqCmEdquSIE/VtwhW5Ndrd7dqh3RU+yjxyQFrQcWsXTObpXYf5oqIBd4CubHWTk6PVzXxT3RzWdnxpHu2lF4nucfJwvM5OdpLdc7NSzVw0Ppete8s5XtcnsmO7TLQ6yHrL6xBwEtgLnA80xGhcScfJJgc3btzDg/MnBk22tTZX2GWz3AwLgzMtmI0GKusd3PPSJ/7lvfWLijhrcD9UVQnJofO1s968bBq6rkfVSlUQeprOtPwNxNCyUtL6uom13JpPxcIXve6p1A6h64TLN77mLx/yys0z2rRDNYKt6eismVeIqijU2lw80pIWt/yZYjYvm8bQLK8z7HR7SDUbuP+1L0Js57EFk/nNy59FnV4kuseJj6bpVDbYKTojO95D6TA/LhzCW59X8Od/HObXs0OEyYRWtBdBPhM4CzgHrzP9g5b/jwESWwCwh/FFIXwOsY91Ow6xZl5oJ6Zndx/hs2MNNDs8ISkYyzYWc6LORlWDI2wO3Zsllei6HnGpThASgUjFrdEe2/q6WTOvMOa2ruvw1K7DrJxdwAtLp7JydgFP7Toc89QOoetEyje2OT1t2mGKUWV9q1W51XML+e//+5x6u5urHt/Nso3FvFlSSZbV5NXj9pyOIJuNBpqdHqoaHX6FiheWTmXVnAk0OtzsK6v1rkLIU1av4FSzE5dHJzvJUiwAcjNSmDZ6AJs+/IbaZinsb4/2HOTJwETgv4FMvCoWhcAfgUeB1JiOLokwtkQhWutcVjU6vNGFn57NluXTePq6Kfz143IuHD+IrcVlKEr4vLVml4cr1u7Eo+mScyz0ORxujQdeLw1yVB94vTTmLVOtZpVbZo7zKxGs2l7CLTPHYTWLikWi09m8d5dHx2hQgmzt92+U+h3iwM/yBUAMAU52TpqZ/P7eB7iqRgfLNhZz+4v7SbcYeOD1Ur/DbRD/uFfgl3hLshQLHz+ZOBSb08PG94/GeygJT5spFrquH29pBnIcuFPX9Wrfe4qibAZGx3h8SYPRoLJmXiF3bjnA798oZdWcCYwYkIaqwKkmB0OzrFiMKk6Pxrxzh7NlzzdcO30kx+vsEZeSy2ts/O7VEtYvKmLZxtMV1pJzLPR2zAbV72z4yMu2YjLE1lFttHvY8XkFz944FV3XURSFl/eWM2fyMPqnxfTUQhfx5b231jxub67UdJ0j1c2s2l4SMg83Oz3+n1fPLeSpXYdZPbcwqGGNqiqM6J9GmtnAxuunoKBQ2+zE5vJwzw/OpNbm4qldh7nvisLYfHGhR0l2B3l4/1QmDc/izzsPc/13R5JqjjbTtu/RXqMQKzAT2AXsVRSlP97UikEtu1wK/C6mI0wSXB5vxOv+n57NkCwr31Q386sXPqaq0cEjV0+iqsHB7S/up7zGxqyCgfz7DwtocLipa3by6NWT+MWz+/yT+pp5hZxoaQv5Zkklq+ZM6HQup6bpVDc5O3WsILjdGpWNDlweDZNBZWC6xd8dLJaYDEpI84bHFkzGFOMwXIrZwAvF5Tz499N6oXnZVq46f3hMzyu0T6S5LHD7oH4WXloxHZdbi3q+MxsN7D1SzdoFk/0Sfz5703SdbSumk24x4vRo3P2Ds1j92uchzq7RqDKon7Wl9bRGk1Hl5uf2SVCjF+K7NyergwzwL+cM47evfMazH3zDDd8dFe/hJCwRHWRFUf4D2IO3OE8D/ok3JSMXuL5lm9CC2WigqtFBk9PDtX/5MCgSUdPkYuXLn1JeY2NSfhbXTh/Jwj9/4J88H5w/keeXnk9Vg5OsVBMpJgPNDg/PXD+Fp98/gqqqnSrckK57QldwuzW+qDgtU+jLnz9zUEbMnWS7W+OPbx9k5ewCsqwmam0u/vj2QX7z4+/E9LwD0ixho5ADkqClbG8m0lw2Njedg1WNXZrjsq0mFk4byartn4XY29yi/KBVjLd+dRG3fX98WGc3sMAuNyOl00ENIbE5UWdHVQhKv0k2xg3K4DtD+7H+3a9ZcP4ZWM2SshmOtiLI5UBgf9UTeLvofQ/wgCgfBeJb3mtyuENyigOl3pZfPDpE3/X2F/fz/NKppFsMnKizc+eWA0EOSXYnL8RIXaSkdakQDZWNjpAC0tYV/LFC03XeLKn0q7b4iHXldVfVN4TYEGku27xsWpfnuHqHC7emhbW3my4e4/85L9uKxaQyMoxWfWtEjaL3cqLeTnZq8s8J84ry+M+/lrBh1xFuuliyZcPRVhjoS+CHeBUsvo83reKcln8vbXkJLfhurEOzrCGFIs1Oj3+brwo6kPIaGw63RnnNaefYt335M8XU2NpuWthaHF/TvCX30nVP6AoujxbWfgIr+GOFIUJDnFjLvEHX1DeE2BBpLnN7NB6cP5H1i4qYlJ/l396ROc7m9HDkZHgN44wUk//nNfMKMYkt9Hkq6u1kpyZv9NjHmYP7MWl4Fo/t+EoULSLQloN8HBiIN8psAQx48499/ya/hXSBSE5piilUMii/v5WHrpwYVAUdSF62FbNBZUC6ucMObThx/NKKBjRND6rqnpSfxfpFRWxZPg1FUfzjFYRImA3hu5DFulAOTusRt5be6gmlrEjXthA/IilUHKpq8quN3PuT73BlUV6HVX48us7Dbx0MsbfHFkzGalb9km0D0s385Z9f++fXQMRm+g7f1tron947Vgd+dt5wGh1u1rxRGu+hJCRtpVhUA1V485B34pV7+xDoB7yBN8XizFgPMBEJlw/39HVTcLg1bnx6D7npFlbNmcDIAWlYjCq/feVTsqxmnlx8HikmlXULi4LyOlfPLWTV9s+4+dKxzCoYGLTM195k31YahS/t46G/lXLt9JFBAvaSiyy0h9mo+JVZAgtIzcbY24yuw3ulFTy5+DwMqoJH09my5xtG5Ixs/+AuIHn7iUk4hYo18wp54HXvjb28xsYvnt3L09dNYfnFoztUEJdiMpCbYUZVYON1U/DoOicbndhdGrrubQ+t6eDWPHx4pJZXP60ISuEQm+k76LrO8To74wZlxHso3cLw/qlc9p3BPPvBN8wtymPy8ORrfhJL2nKQ7cDggP/nATcA2Xijx+4YjiuhCeeUHq1u9hfildfYWLLhI/KyrbywdCpVDU6unT6SJRs+8qtYbLrhfGqbXZyot/P7N0rZV1ZLyfEGnr5uCiXHG6Kufm4rjcKX9nHvTyZw5fr3JRdZ6BBNTo9fi9hXuPTA66X84eeTyInxua1mldnn5PmvmbxsK48tLIq5HrHk7Scmvrls87JpHKu1kZNm5leb97OvrNa/T3mNjVNNTobnpEZ0TMMpYfS3mrl15rigoMXaBZNJNavc8uw+/znysq2snF3Aso3FQat6YjN9h3q7G5vT06sUSeYX5fPRkVP86/P7ePXW79IvpU8nBwQR0UHWdd2lKMobeNMwDHjzj7WWn3PwRpD75ONxoFM6KT+L5ReP5oycVFbOLmDdjkP+CbW8xoZHh1tnjg0qzHuzpJKS4w3+ydZHeY0Nk0HhhaVT8ejedI0BaW3nQPqWHlvrd/qizqqqoOu65CILHcagKGG1iHui4UGzU+OmVgWCNz1TzPNLp8ZUj9jp9pCbbgl6KFi345BcKwmAby6bt+591i8qoqrREfR+XrZXZm1IZkrY4yNFenPSzSHFqCs27eX+n54d4oBnWU0hq3qJVOshsp6xxaeB3JscZKvZwC2XjuW/tpfwy+c/Zv2ioh5Jo0sG2vwt6Lr+a7zpFAd1XW/Wdd2u63qTruvfAGXAr3tikImGzymdlJ/FHZeNZ9X2Ei598F1WbS/hjsvG+4tF8rKtpLRUPYebQFtfZHnZVlRF8X5+lpWBGSntTm6+pcfA3LnWUefOdpgS+jZWsyFsu+eekATyaOEf6mKd22k1G7jr8vFBnfTuuny8yCAlCL65rHXHUl+q2tbisojzWnWTk4f+Ftyd8aG/lWJ3hXdwU0zBn+NrHJKo82tb9ShC93C8zmsnOb0kB9nHuEEZXDvtDN7+opJbn9uH3SUBAWi/1TS6rr+v6/rhMNs1Xdd3xmZYiY3PKW0dGS6vsXH31gMsv3h0kH5qqiX8BJqTZg5xPqoaHfzHtgNBE1tbBSCBslQ7776EbStmhOS+ReNER4sUo/QdsqxmBvVLYdWcCf5CpUH9Usiyxj56YjZGKBCMsf6yW9NDlGTu3HIAt9h5QuCby6oaHf6OpW/ffhH3//Rsntp1OKJGMYCmaay4ZAzmluiY2aCy4pIxGCMopuRmWILmzHULiygYksGgfsHOUXfOr10hUqpHdZMoFHQXx5O8i15bfL9gMIumnsFrn57girU7+fx4fbyHFHdi0mNQURQj8BwwFCgFVgBbgHzgAHANXmWMdrfpup5wdyafU5pmMYSNPJw1OMNfJKfHMdUYAAAgAElEQVSqStjmA+sXFeHRdTYsmYKqeCNmT7z3Nbu+rmbl7IKgQrvSigYe+lspc4vyyUkzY3O6GZpp9TdraE9zs7u0XaUYpW+hqgrDs1NJMRmCOun1xN/aYlTYsOQ8yk7ZSDUbaHZ6yO9vxRLjAkGXO7y0ncstfZESgdZzmcmoYlQV0swG7v3JBHTdm2IQbn5TFCUkJ1ABLCaV9YuKWLYxuHD62d1H2LxsGrqu49F0fvdqCW+WVIbMe4minZ1IqR69leO+JiG9QOYtHD88ewhDMlNY9+4hfvTwP7jqvHxunTmWIZmx1b1PVGLVhPtfgP26rs9XFOU14GagXNf12YqibMerqzw8ym1vxmiMXUJVFawmY9j8X6vZGOSwqqrC2Nx0Ni+bhtujYTSoDEg18WVVU0iXsusuGIHFaCA33YLT7fEvC7ZWoVi/qIizBveLehLuDuF6KUbpW2iazjc1zRytbvY7qfYcDyNy2m+U0FVcbp0Gu9tf+JqXbeWPP59EhiVWU5aX9nL6hfjROr92SKbV32o6mgd3XddxurUgm3royom4PToD0sxBeee+wulrpo/Eajawv6yO6y8YxdyifNbtOBQy7yVCYxCx3dhzvNZGVqoZo9p7c3QnDc/mwfnnsHVfOS/uKWfbvm+5deZYln53FMY+lpscq2/7OvA/LZHkLGAy8LeW994GLsHbaCSabQlLtEtrmqZzvN5Gs9ODW9NpdnqobHKG7VJ29JSNRX/5kLsuH0+axYDT7eHffnBWSCrHso3FnKi3o2l6TNMeAj/bV8AUiEQoei+1NicV9XZWvvwpVz2+m5Uvf0pFvZ1aW3RLtl2xS4+uc8tz+4Js/pbn9uGJ8YJSoiyXC8Fomk7piVb5tSca/E5zpNSCQBt0azq3bd4ftN9tm/fj1nRUVWVrcRm1NhdZVhPLLx7NrIKBWM0GjteevgZ8dSa+AEYiIbYbe07U23tlekVr0lOMXDttBP9z5UTOHpbJA6+XsvjJj6hrbrtpWW8jJuEYXdcbARRF+QBvw5EcoK7l7XpgfAe2haAoylJgKcDw4cO7/wtESbRLa/V2JzXNLlZs2uuPXGy8fkrY5TBfp707txzg2RvO5+o/fcCD8yeG3fdYrQ27y+PXX+5o2kN7Fc/hIjM+7dFA6SOJUEQmUWy1M9icnrD5uC8snQrtKEl0NR3H7QlfpOf2xNZBTpTl8niQyLZ6stHBjRtbOcEb9/z/7b15eJRllvf/uWtLKgtJyMJiIiCbRgxCkEXsaZeW1hblZ4Mr4K6gtva4d08PvbyM8xNpx1ZbAX3bDXCnbR26cUMZR8UNEFqjgCySsCWEJGSp1Hq/fzxVD6mkCiokqaok53NduVJ5qip1nqfOddepc5/zPfz15tPxRJn46PL4qPD4zNKINfecGfFx/oAmx2lvI/W2eFYpGs2cVomM+1ZsYv60UUm37vUm302Ur+6pdfW4Br0jkZ+Zyp3njuSDzZU8/dEOrn32c164cWKbBtaeSpdkkJVSuUqpFOB0DN3kUUBW8O4s4EDwJ5ZjbdBaP6m1Hqe1Hpefn98VpxAzsYylbXT7zeAYjEU22mjT2uBY6YoaF5X1bqP+0R+I+Ni8DEPA/uF3N7e7MSOWjudImZl7XtvE7ecMN22QDMWRSSZfbS++KEoS/hgywR1tGLJaooyajsOHfW8dNZ3MvuqKojTR7PXjD+iIvuIL1g1ff8YJhrKQJqpPHXRF3tFr9kQOvofkpSflutdbfDcRvhoaEtIbMsitOWtkAb84exgbdtXyqxWbEm1O3Oiqgr67gDKt9TKlVBNwPzAFWIFRRvEwRr1xLMe6PZECjUdXb2XRzLHc3CKrvGB6CX8MjnwMaXoC5Gc6WH7DBKrq3VQ3elixrpxrJw/hjpe/oqrBzaKZY7l28hAzsxtL2UMs9cTRmj6GFmTw8X1n9egMhQA2i2JKcQHTS4vM2swV68pjClI72jBkCY6abll3v2B6CeJqvZPQF6bW9bUWi6LO5W3T0JmTbueBVd8yvbSIu17dyAM/P4WaJnfEyZCpDgtN7sj+6tc64uumpVhl3etlHGr20dTDhoS0hwlDcplR6uLVdRWcf8oAfnpy/6M/qZvTVQHy48BSpdStwDbgL8AKpdQmYCOwGnAAP4/hWLfHbrW0WWSrGtw0uH3Mm1pM/z6pZKfZuf/vZWworzW391weP3f9ZDgeP1z/3GdhW38ffLvfLHO4efl65k8bxd0/Hckf395MVYP7qNt/sQQw0Zo+nHZrwhtShK4n1W7htrOHh32JWzRzLKn2o288dbRhKKDhuU92hDVOPffJDn534cnHfD5C98VuiTz23GpRPPfxTmafPjis+e6hS0ZTVe8xS9b6Z6VSVe8mzWFl/rRRZiCd5rCiUFhU5ADcqlQbBaKQfKfQu9gd9I3WfTi9iYtOHcjnOw/y73/7msnD8sjo4qbpRNMlJRZa691a67O11pO01rO01m6t9VStdYnWerY2iOlYV9gXbwoyUlg8q7SNqP2Db21mztJ1THv8Y5SCK8YP4oO7z2Tp9ePRWtPg9nFxaWGbiWJzl63jvFMGmP+/osbInNy3wih/iKXsIRZxe2n66N24vQEzOIbgNLvl63F7jy55luO0t/H5xbNKyXHGJo+kFFx9+pCwgR1Xnz5EMsi9lL5pDvIyU8I0uXMzHDz70XZuPnNomzXyrlc3cvs5w6l1ec1AN81h4/dvluHxG/7r8Qf4/ZtluDx+LBYiDh6JRWde6B3sqe2ZQ0Lag81i4frJQ6iqd/PcJzsTbU6X07PD/yTBZrMwsiCDl2+aiC+g2V7VaMoIgbEY76pu4tpnv6Awx8n8aaPM29Ga+VpucxfmOOmb7uChS0ZzXI6T/u2YwNdm7GqL4Lc3NX0IbfFGqUH2xlCDXOPy8ujqLWEZ4EdXb+H+i0ti2n1QqIgZ5D9cNOqYz0fovtjtVgZlO0mzW3H7AqZu/CvrKjj35AER/fT43DQWrPqWhTNKSHNYcdgsEUenK6XQUXYsfn/RqKSQcBMSz57gFL28jN6dIBreL5MxRdk8+eF2rpo0iMzUnqkJDRIgx4VAQFNe6+KH6ibyMhzkZjioanADmNuBD6z6DjicDQ7dDjXztd76C8UohTlOnpg5loVvfxdRxD4asQa/8uHQe7FFqfu0xViD/E5ZJe+UVYYd/92Fsdcg33zmMFPqLaSDLN/NeieBgGZbdRONbh8zFq8Nu6+60RPVT68YP4h+fVLJz0ylqr6ZP185hppGb1itsjXoU6215hdMLzHvE4TdNS7sVkWfGHfBejLTSwv59799zQuf7WLOj4cm2pwuQwLkONBST7aixsWU4gKev248Votie1UjD6z6LiybHFKyAKOZb/Gs0jD5oUWzSgnoAKvv+jFaw6tf/MD00iKuP+MEal1eHn53c0yZOgl+hSOR6rCwaFapuX0d8r1UR9fXIPu1RinC6kWVgkDPqLoS2kmoqXje1OI2frViXTlPzBwbJqO5ZFYpLo+PEf0yzIEidpsFb4RBIXabhWZvIGoGWRAAdgcl3ixKvjUNzc/gpAGZLF37Azf86IS4qAslAgmQ40BrPdl3yiop21vP8hsmmNt+QJjOcIiqBjepdgsv3jiRgNbYLIp3v9nL71d+x5iibJ6YNYbLxg/iYKPHVLi4+vQhBAJGnV1LrWOnw4ovoPH6AlIu0Us4mtb1kfD7NX1SreY49IAGu9U4fjRiKeE5ElrDEx98z/TSItKw4vEHeOKD76VJr4dyND8NDSnqk2pj0cyxPPb+VqaXFpGb7iAvIwWv3x/2ZSo91QpaEWgxejraoJC/3nI6ViUZZOHI7K5xkSf9NyZTivvzyOqtrNlcyTkn9Uu0OV2CBMhxwK8j13LC4bq3/n1SyUm3U+/yhgXMi2aOpbrBw12vbjQX7sevHMt//2IyDW4f1Q3esOzy41eOxWZV+AKa2iY3e+vc3Pj8l+RnpHDveSPDusDbM7hB6H50dFiH1nCgwcMvX/rKfP4jl59KYbbzqM/taP26xRI5YOnBE157LUfz00BAk2q3sGT2WNw+jdUCvzxnhDnAI5QJDqmrpNotVB1ym8Fw6P/1TbNHrqkPJgwiZZDvv7gkEZdESEJ217o4aUCfRJuRNIwbnEPfdAfLPv2hxwbI8nETB1LtkRUjnHYr919cwqiBfRiQlcq2ykY8fs0z15zGR/eeybypxdS5vGZwDMaCfusL69lT10yzN9BG3P7WF4xtxsue/JTygy5ziMjcM4eawfGYomzmTS2m0e0zx1ULPY+ODuvwa20Gx6Hn//Klr2Ie99yRoQVaqzbj1e9bsQmt5ctcT+Noo6J3VjdS7/ax75CbK576lH/uPtRmut0dr2yk2Rvgsic/pdkbaJMpvvH5L/FHGRQS+vJ2x7kjw1RT7jh3pCj2CAB4fAGq6t3k9WIFi9bYLBbOGJbHh1sOUFnfnGhzugQJkONAXnpKRLm0vIwU8jNTGJDlpKrezbw3vubiJz7h2me/oNblY/3OavpnpUbMemQ77aQ5rFHvC0lyTS8tAjCPjSnK5u6fGh8EMxav5dIla9tM0BN6Bh0d1uFN0LhnMGqNI712D1F+FFpwJD+tbvTwQ3UTPj9mjXFoLWv9+FBzc7R1UWsdVXpQ5NyEI7GvrhmNKFi05l+G5+PXmje/2pNoU7oECZDjwNEW3+pGT5uMyKOrt3DhqYWUH3RFHUkd0viMdF/o/wzLz2BMUbb52LlnDm2Tmbvx+S8lk9wDiUXr+khEG/ccj6AhNLih9WsraZDpcRzJTz0+Y5iHRR0uSzvauhftfqWUKT348k0TmTe1mEdXb6Em+LzeMqZZaD8VtU0AkkFuxXE5Tobmp7NifUWiTekSJECOE5EW30BAU1XvpsljTNQbU5RtPn56aRFzl63j0dVbIwrYL16zjcVrtrFwRuT7Qn/vOtjEveeNZHXZfh6/cgxD8zMiZlf21Lokk9zD6Oigl9D0spbPXzijBHscAgerijy4QZqmeh5H8lOljCl3oWmkAIvXbGvjGwtnHF73Iq2Li2eVYlVGg/Scpeu47MlPmbN0He+UVca8oyL0XkKfmaL61JYzhuXx7d56vq+sT7QpnY406SWISI0pC6aXmANEctMdVNS4qKhx8ce3NzNvajEDs530SbWFjaTOy3CwZFYp6Sk2AlrzwKpvzftC+spVDW5WzJ3EgUYP5Qcj6ypnOe3UubzsO9RM/z5HHzQiJD8dbZRz2IwvdS3VAfIzU3DYut43oo2alkEhPY8j+anDqnA6rMxf+Q0Lppdw34pNbCiv5blPdrD8hgn4AxqLUlgtkJ9pfPGranDTJ9XGsusnUNPkobLezaOrt/CHi0ZFXPvsNskTCUem/GATFgW5UmLRhvFDcnl+7Q/898a93HFuZqLN6VQkQE4QkRpT7luxiXlTi5m/soz8zBRzMd9QXsucpet4dc4kAoEAV4wfxPVnnECTx2/+PLJ6C/8+tZjfXngy9553IvvqmsP0lX0BzZyl68jPSDE/aEKB+bEMGhG6Bx3Ruvb6NG5veHbN7fXj9XX9smFRcO3kIWGqKwtnlMigkB5KND/1+LXZiFxV72He1GJy0x30z0qlusHNrS9sCFvHfn/Ryew6aEx7/M9/lDG9tMicnDd/GiycUdLGp2IZfCP0bnYdbCIvIwWbyOi0oW+6gxMHZLJy0x7+9SfDe1QZnATICSJaY8rwggzmTxtFQGsevnR0mFRR/6xUrnjq0zYZkGXXT2B6aRE+v8ZuVTz41ndhE8wKc5z4gmODW2aks512BmY7mb/yG/PxFTWG8sXvLxqF1lr0knsxnoDmkdVbw7SIH1m9ld/GQYvY7Q/w4FubwzLID761mUeuGNPlry0kDzpCs6Y/WAb2+AffhyUYblm+nhdvnMjlT35KYY7T9B0w1sBoPvXnK8dAenzPS+helB9skvKKIzDphFye/ngn3+2r71FSeBIgJ4hok8a2VjYwZ+k6CnOcvDJnYtj2drTOfqUwss4ZKdx+znB+/bOTuO6ME1gQLK9YML2EvXXNbTLShTlOll4/PiyYHlOUzdWnD+HSJWtFL7mXY1Fwy1nDqGk0mpgcVgu3nDUsLnXAqXaruWUeIj/TYWrdCr0Dp8PKM9ecRnaanSynnQdWfWvudC2YXkJVvcfcJauocRHQmiWzS1m8Zhu56Q5zDPVTV40j1W6lqsFtZpShfU2rQu9l18EmTjku++gP7KWMH5LLs5/sZNU/90qALHScSJPGQjXIYCz29c0+PP6Amb2rCS72rYPqvXXN5GekcPdPR4aVTiyaOZYGt8+czLdo5lhubjGO9eFLR5NmDw/Uo6lcvDJnkmSUexlWpXB5/GGjeY0yh65/7/s6Hdx+zoiwITiLZ5XS1yk1gL2FQECz/5A7zP9aBsWhkrRQwFuY46T8YBPzV5axcEYJA7NTKcpx8votk83G1I5MdxR6J00eHwcaPBRIBjkqWU47I/tn8o+v93HnlJGJNqfTkHRMgrBYFMPzM3hlziQ+vOdM5k8bZTboQVBOSykWr9mG1x9gaH660ZA3O1zHc8msUhas+i5iYHvz8vUcavaxobyWqgY3eRkO5k8bxcs3GZnp3IwUcltpNIeaA1sSUrmYvOADLn7iY1G76EaElFJ21zRRVe9u1/vmDeiwEekVNS7ueW0T3ji89zUub5shOHOXrTMluYSewZH8M1qfxtwzh5p/h4Lb0Beoh97ZYvqpzWKhX5bTVA0SrWPhWAj5X0EfCZCPxIQhuXxf2dCj1Cwkg5wgAgHN1qoGbnz+SxbNHEuaw9j+G1OUze3nDGdQbhqpdgu/ueAk/vXlw6N+n75mHC/dNBG7ReHX4A8EuP2c4WRHGaNakJnCM9ecxpC8dKwWC6OLsnB5wjvFW3aQq6D+bOssdWj6Wiij/Potk6UmK8np6KhpfyBySU88vhx1dMiJkPwcyT8BXF5f1EFIcFh95+WbJtLk8aMUYeUWXn+gzWt2pGlV6J3sqjY0kAsyUxNsSXJz2uC+wTKLfdx2Ts9Qs5AMcoJomR1pcPtItVt4+NJTeXDGKQBBfeQAT364jfyMFJbMLuWhS0azu6YZh1VxoNHDpUvW8qMH1zDvja/JctojiuP3TXcw742vOfOPa/j5ok/Yf8jNgBZZFQjXaO7fJ7WNJumimWPpk2ozdZolUOkedHTUdIrNEtGnHHGQxerokBMh+Ynmn7UuD5v317OntjmiD3j9AVO1otlrqPgUZDrw+gPmGiW+InQWuw4GA2TJIB+RvukORvTLYNXX+xJtSqchAXKCaJkh8wc0t76wAV8gwIEGD/Pe+JrLnvyUa575nFvOGsavzj+R+SvLuOzJT5n3xte4vAHmLA3ffn5g1bcsaTVGdcnsUu7/e1nEACna1mYoo/zXW05nzd1G6cdv3/iGe17bxN0/HcmYoux2f/h0ZJtfOHY6moVVEHFQSDw2pDs65ERIfiL5Z35GCi6Pn0a3jwFZqRH9b2hBBstvmMCf39/KhX/+mHlvfE2D288TH3zP3DOHiq8Incqug0047RYyU2TD/WiMH5xL2d5D/FDdmGhTOgV5xxNAIKBRSvHa3ElUN3rIchrlEf37pDL76c/DAtqaRq/ZpBI6VlXvbvPB8k5ZJfOnjQoT2w8EAmEKFaHne3z+I269WywKhWLWXz4Le537Vmxi/rRR9M9KjfnDp6Pb/MKxE00pJdYvN82+KFJrl5/aVSabdHTIiZD8tPbPMUXZ3HveSC570pCyfG3upIj+95sLTqK+2RcmTXnXqxuZN7WYE/tnmk154itCZ7DjQCP9s2TMfSyMH9KXZZ/9wKqv9zH3x0MTbU6HkQxynAkFjJcuWcuMxWuZv7KMgIYpxQX4I8i4pTmsbY6FpItaEtI69voDpvC9PcoWuUUp9tU189Alo1kyu5T8jJQ2W+/Rso9DCzLaFdx2dJtfOHY6moW1WZQpixUazVvV4MYap8Aj0nh2oefQ2j9vP2d4WFNodaMnov/lpjsYnJtmllPA4YY9p90qviJ0KtuqGuifJfXHsZCfmcLQ/HT+8c+9iTalU5AAOc5EChhvfWE9v7mgmAMN4YHvmKJscjNSeG3uJJbMLjU/EFasK2dxq3KKhTNK+MULG7h0yVq+r2rkN69vYn+dm+evG98mQGr0+Mwyjvkry7j7pyPJz0gJ23qPVgPqtFvb9eEjzVaJo6Nd+xYLLJgevsW9YHoJMkxK6Axa++fQgvSwtWLxmm1t/G/hjBLufGUjs5/+3Cz5Ct3Xr08qeRlSJyp0Hm6fn901LgZKgBwz44fksqmijvJg7XZ3Rkos4ky0gPFQs4+B2aksmVXKnGXGSOh7zxvJNc98HqYB+twnO7jtnBGkp1h49trx2KwKn1/z5P9sMzu4Q/qgNy79kr/efHrYNrXVAhf9+eM20knzp43C3qL5KpJO87HU9XV0m1/oGB3p2tcB+HDzfp655jSsFoU/oHnty10MyR3SyVYKvZWW/llV7w5bKzaU1/LcJzt49trxWC1Q1+TF5fXzq/NPpNbl5blPdjD3zKGm7rEtHhNshF7FD9VNaGBAlvOojxUMJg7py4uf7+If/9zLnG5eZiEBcpyJFjCmO6xc+dRn5GekMH/aKIYXZPB/Vn4TVn/33Cc7+N2FJ+OwWfi+ssHcjgwFz1srG9hQXmtKIVXUuHB5/aQ5bAzIcmKxKHbXNEUM0AflpkFAU3moGa8/gF9r+mc5eGXOJHz+ADarhYKM9m9ddlagLcQfu83C9HFFVNS4zGmO08cVhX2REoTOIsdpZ/Gs0rDhMFefPoR7Xt1IfqaDu6aMpKbJS5rDak51zE13MG9qsVmbbLNYRMZN6DS2VzUAMEAyyDFT0CeVofnprNwkAbLQTiIFjI9fOZYHVn1LRY2LihoX1z77BX+//QyuPn1I2GS8BdNL0Gga3L42AxxaTpUqzHFS6/JSmOPku331zF9ZxpLZpeSlO1BKMaW4IKx5rzDHyZ5aF8flONlX18w9r20yM9gtg/Bjaa6TZqvuiz+gqQ6qqrScpNcn1Z5o04QeSI3Ly6Ort/DSTRPZXeOi1uU1hye9eOMEDja29cVAGuaaV93okUBG6FS2HzDUGKQGuX1MPCGX5Z/tYueBRgbnpSfanGNGUkFxJpKMmkXRRm3CYbW0mYx334pNaG0oTEQT0A8F0ivWlbNgegmL12yjosbFnKXr+KqijkuXrOW2c0YwpbgAOFxX+vzanVgtimZvgIcuGc2DM0pY891+5k0t5uWbJjJvajEPv7s5pua61rJugDRbdUN8USbp+USmT+gCPD4/75RVooC7Xt3InKXrzLKx/lmpEX1Ra8LWPCndEjqT7VWN5KTZSXNILrE9TDohFwW8uXFPok3pEPKux4lAQFPd6DGzqHnpKZAO6Sk2PD5/m7KLJk/kWuUDDW5qm7wRyzT69UnlmWtOo9nr59fnn8Sdr2wMmywVKrt4bPUWfnfhyfzq/JPMutJ7zzuROpfPzNBMKS7gtrOHc/Py9WbG5omZY/H6/eypdWFVYLFY2mSDjzYdq+U1kExy19Pa79pzzRM5SU/oHbT0z9Dult2mzF6M0BpiUZGTAkrBvKnFPPfJDu44d6SUbgmdyo6qRskeHwO5GSmcOCCTv23YzW1nD+u2EnkSIMeBIwWN+ZkpBAK6TdlFTpo9YhBcWe9m8ZptLJxR0qYG+Y6Xv2JDeS2FOU7mTxtlBseh59a6vIwpyubq04eYWqOhwDczxcY1z6w1X296aZEZHIPxYXTL8vXMm1rM/JVlZsPgHeeONMsuAgHNvkPNNLp9zJtazOI1RuPgjc9/yZu/mMz+Q24JnONIRzWo7ZbIY8dt8t4InUBr/5xSXMDvLzqZQy4ffZw25k8bRZrDSq3La2aKW/ui3Wph1MA+jD2+RNYNoVPRWrO1sp7TBvdNtCndksnD8vi//7uDr3cf4pTCrESbc0xIiUUcOJoWcCQ5roFZzjYatqGSiQ3ltTz41maWXj+eD+89k+U3TOC5T3aYwfGimWMp6uukMMfJmKJsnrnmNJ67bjzpDiv3njeyTenGLcvX422VLQxlm1vSMgt934pNXDVpMPvqmqmobaKyvpmd1Y1h+s4hGaaKGhcuj/+IY2UvfuJjJi/4gIuf+JjN++slS9kJdFSDWin402Wnhvngny47lW6aDBCSjJb+eWlpIb86/ySaPAFAsWztThw2i1lq0ejx8dAlo8N88aFLRmNVSOmW0CXsrWvmULOP43PTEm1Kt2TCkFxsFsWK9RWJNuWYkQxyHIhFCziSHNfIfpm8MmcSLo+fXQebzIYVgKoGN/6AMczhP/7+DdNLi7j+jBOodXl57P2t/GHaKF68cQJ1Ll9YV/iimWPJz0gJs6eixoXbGwjL0ISa/FpnbGpdXvM5x+U4mf2XwzJ0C2eUmP+7ZePg/JVlEYegHClwfv2WydKN3kE6PGpaKexWZWbymjx+7FbVbbfLhOQi5J9jirKZOXEQVz0dLmn5xobdpoqPw2rhLx9tD1P1+ctH27n/4pJEn4bQQ9m8rx6A4/tKgHwsZKTYOG1wX17fsJtf/+xEUrphf4BkkONAtKEbR2sosVgUWmvufnUjDpuFqga3+dwls0vpm24noDXvlFWGTZt6p6wStzeA16/N4BiM4Ojm5eu5/ZzhbWzZW+cKE+Vfsa6cJ2aONf+eUlzA89eNpyAzhSWzS5lSXIDWkB8U5g81zcw987CsS2i61VNXjSPVHvkaRAucZZBIxzlWvwsRCGge/+B7PP4AAB5/gMc/+F6y+0KnEPLPuWcO5dYX1pOfYawtD10yGo8vwMVjjzPXtYVvf8cvzh7O/JVl5oCjX/5khNQcC13Gt/sOAVCUIwHysXLmyHzqXF7eayVC0F3okgyyMlJMzwIjgUrgSuAloAjYBFwFpACvHe2Y1rrbfxp3RDAMS4IAABcFSURBVAvYYbNS1eDmj29vNrMnTR4/A7JS6Zuegs+vI2d6mzzkpDsiBp/D+6XzzDWnmVnBnHQ7f3izDMB8jcIcJwGtmT9tFAOyUvAF4KqnPyc/I4XbzxnOr392EnVNXu49byRXPPWZ+b+znfYwOwZmO+nfx2hyiHQNQoGzDBLpfDqqQa0U3DllBDaLFYsyGi/unDICJV+rhU4g5J+Nbh/5GSn87qJiahq9OGwWcjNSSLUrll0/nufX7uS2c0bQv08Kz183nvpmHzlpdgYGtd0FoSvYvK+evAwH6Smy0X6sjBqYRV6Ggxc+/4ELSgYk2px201Xv/GTAprWeqJRaA1wHVGitpyqlVgLnAsfHeOydLrIxbnREC7hlkBPS+3zqqnH0SbFTVe9Ga82Ts0u5aenhMools0vJz0jBH2gbPE8pLqC2yRemJ/rwpaMBY3JV6DVev2UyuekOrBYLAR1g6/5GHrtiDFlOOw+s+pZ3yiqNko1ZxgjsUP1zk8fI/Ibs7N8n1TzPSNcAIgfOkhnqOB3VoLZbLXh9mhuWH976XjRzLHaZNS10AiH/3H+omX/72Ul4fYGwdemhS0YbwfHZw1n5VQVXTx5Cis1CZrZTGvKELufbvYekvKKDWCyKs0YW8Oq6CrZXNXBCfkaiTWoXqisStEqp4UCO1vpzpdQ7wDjgRq31CqXUnUA+MAhYcbRjWutfH+m1xo0bp7/88stOP4dkorVUV47Tzt5DLtw+jUWBzaqwKIUCMwgKBDQHmzxsqwqfuPf8dePNWr8QIdWLa5/9oo3Swf46F5X17jC5twXTS8x66MIcJw/8/BR+9dd/8tRV4+jXJwWXp33BWEekyI6BhH2qdjdf3VPTxKVBtZMQhTlOXrlpIgNl2zEe9ApfrW5sxuUJcHkEXwutLcuun4DTbqGfjPxNVnqUr3p8AYp/+xYXlAzg8tOO79T/3duobfLwixc3cPWkwfz2wuJEmwPt8NUuySBrrbcCKKUuBhzAOqAuePchjNKL3BiPtUEpdRNwE8Dxx/d8523dwFfb5KamycstrTSKj+/rJDstBZ8vwHf762n2+nnwLaM0Y2h+OuUHXdS5vBHLLk7IT+flmybS5PGTYrOYQasvoNvIvbWc2ldR46Kob5qZcbZYFLRzcE6kBsWeQnf21dbKJmC8/16pQe6RJMpXG91+ApqIvtY/K5WKGhd+rcXvBJOu9tUt++vxBbRkkDuB7DQH4wf35dV15dw5ZQQZ3ahkpcv2SpVSFwG/BC7EqEMOCeFlAQeCP7Eca4PW+kmt9Tit9bj8/PyuOYEkptHtN4NjOCzV1ug2yhsqG9zMXbaO6kYPVQ1u5ixdR3WDh2uf/YLKenfExq3yg01c9uSnXPvsFzyw6ls2VxrSa5X17qhyb6HnWoMBrmx5tqU7+6o1qIPcktD7LfQ8EuWrVqUgqHPcksIcJ1algoNCwCLqKUKQrvbVr4JqUcO6WUlAsnL+qP7UN/t4+YvyRJvSLrokQFZK9QfuAS7QWtcDq4EpwbvPBj5oxzGhFb4omb3QCGCvP0BFjYvFa7aZyhQh2baWx+CwnuhD72wx/9f00iIzOxwtoA79v4UzSrAHh4QIPQuH1cLCGeG+snBGCQ6r1CALnUMgoLFbLThsKqKvHWjwsHBGCSk2C/K9TIgXX5XXkuW099idzXgzvF8mJ/bP5C//ux1fUBWpO9BVue6rgQHA20HN1KXAcUqpTcBGjEDYAfw8hmNCK+xWC1OKC5heWmRqgq5YV47Noqiqd+OwWijMcbKhvJY3NuzmmWtOI9VuMce3/vHtzcyfNorBeemk2Cw89/H2sKl7uS3UL0IBdWi4SKicQwHzp43C6bAy742vwybqHYk41xsLHcBqgfzMlDAd5PzMFCQ+FjqDQECzs7qRH6qbKMxJbeNreRkOapt85Gem8NGWKiYOzUu0yUIvYf2uGobmZ4jmeycytWQgf3xnM298tYfppYWJNicmuqoGeQGwoNXhJa3+dgNTYzgmtCI/3cFt54zg5pYDQGaV8td1Fby8roJX505i4YwS1ny3n8vGD+Jgo4ftBzys31nNizdOxB/Q7DjQyJ0vf0VVg5vFs0rZUd1kKlMUZKaY6hcbymvNgHpoQTqpNitaa8prXHj8Af7wZhkbymsp21t/1OEeHR19LMQXi4L0FCvDCjIIaI1FKWxWJJMndAq1Lg/7DzWbyhVTigv4zQXFeHwBCnOsaDRKKQ40eBg5oA/pKSL9KHQ9dU1etlc1ctk4GTHdmYw9PpvBuWk89v5Wpp06EFs3yLR0n2ppwaS22WcGxxAcALJsHfOmFlNR42J3jYvX1+/mqtMHt5lOta+umTte+SqsRGPusnW8dNNEfnvhyTgdFrJSwvVzqxrcpNot+PyavOwU9ta5mLF4bZhNsQz3iDb6WKbmJScen2bngaYwFZSFM0o4Ia+dXZiCEAGXx2/6FsA7ZZWU7a1n+Q0T2HWwrd91o51ZoRuzsSJYf1wg9cediVKKn48t5L/e3dJtssjJH8ILbYg2QjjUOFfd6OH8Uwa0maJ334pN5GVEHh6yu8bF5U9+yt5aQ1s5N93B/GmjePmmicybWsyDb23mqqc/50Cj+5gntHV09LEQX7wBHRbAhKYlipqA0BlEm6JpUSqi33kkQhbiwBc7D2JRcEK+JAI6m3GDchiSl85/vbsFdzf43JcAuRsSLUCtdXkBY0z04Ly0iB8+gSjd4rVB+be5y9ax51Azuw42ce2zX5jjqzeU11JR46LZGzCHl7RsqIlluEdHRx8L8cUfpRlUGjKFziA1ynogfickkg+3VDGsIIM0h2ywdzZKKa4cfzy7a10sXftDos05KhIgd0MiBagLZ5SweM02CnOc3HHuSNIdtqgSXQ9dMjrsuQumG88F44Ooqt5NdaMniuxS+IS2j+87i9dvmRxTHfGxBtZCYgg1e7akMMeJvRvUjgnJj82qePjS8LVo0axSapsirz2pdvkiLXQttU0eNlXUccpxWUd/sHBMjDoui9FFWTyyeisHGtyJNueIyFekbkjrEcJ2mwWbRfHnK8cccYTz41eO5YkPvmdrZQPzphYzsl8mOw40mlPxwPggqm70RFSvWDijBKfDatrQ3rrhjo4+FuKLRcFjV4zhthc3mD7w2BVjpElP6BRcHj//+Y/vWH7DBHwBza7qJgKBAF6/ZtGs0rAm5CWzS8nLkD4FoWv5+PtqNFBSmJ1oU3o0sycO5r4Vm1j41mYWzChJtDlRkQC5mxIxQG1VMtUyGFVK8dzH2zmnuB/TSwupdXlZ890+SofkURX8FleY42TxrFIeXb3FVK+YN7WY3HQH2WkOLAqynR3L9vbkqXk9DWVRpNgtYdJbKXYLSiJkoRNw2KxUNbjZU+vCZrEwODcNq1VR2+TlsdVbzLUnPzOFATKISIgD/7u1ijSHlaEyIKRLOS7byfmj+vPyl+XMGFfIaYOTUzFEAuQeTMtg1OcLcOGphWbjXigYHpGfzitzJuHzB7BZLeSnO7jj3JGU7a1nQ3kt81eWsWRWKX3T7WQ7Jdvbm8hJtVMXrGsPYbdayEm1J8gioSeRm+7g+evGU93gNpV1jJ2uMcybejJaa6wWRUFGCnYprxC6GK8/wNvf7KOkMEumhcaB6WML+XzHQe57bRP/+OWPkrKESgLkXkKNy9tG1WLusnW8fstkBmaH1/tJGYQAhpzgtc98EdYwVZjjNGT5knAxE7oXFosiI9VmSlGCsS7d+sIG/nrL6RRkpibYQqE38dHWA9Q0eZksA2niQqrdyvVnDOH/X/Ud//XuFv7tZycl2qQ2SLdNL6E9EmuhzPNxOWnky9Zmr0Vk+YSuxusLRPQxr08k3YT48revdpORYuPUIqk/jhclhdn85KR+PPXhdj75/kCizWmDBMi9BJFYE9qL+IzQ1YiPCclAXZOXd77Zz4QhfbvFhLeexKyJxzMw28ntL21g/6HmRJsThnhCL0Ek1oT2Ij4jdDXiY0Iy8Nzanbi8fs4t7pdoU3odKTYr//qT4TS4jQnBzd7k2aGUGuRegkisCe1FfEboasTHhETT6Pbxl492MPb4bAblyvS8RFCYk8bcHw/lT+9t5c5XvuKxK8YmRaOkBMi9CJFYE9qL+IzQ1YiPCYnksfe/p87lZdqpxyXalF7NhCG5zJrgYdlnP+C0b+LBGSUJD5IlQBYEQRAEodexsbyWJz/cxlkj8xnRLzPR5vR6LigZQLPPz2vrKmh0e/mvy05N6MhvqUEWBEEQBKFXsau6iRue/5KcNAczJwxKtDlCkOljC7lq0iDeKdvP//f4x5TtOZQwWyRAFgRBEASh1/Dp9mouWfIJzR4/9513IukpspmeTJw/agD3/vREKg+5uejPH/HbN75mT63r6E/sZMQrBEEQBEHo0dQ3e/lkWzWvflnOe99WMiArld9ccBJFfdMSbZoQgdFF2SyYUcKrX5az7NMfWP7pLv5lRB4/Ke7HuEF9OSE/HXsXS/JJgCwIgiAIQo/hP1aWUVnvpsnjo6bJy55aF3vrDI3dzFQb08cex9SSgUk53lg4TJ9UO9efcQIXjR7Ie99W8un2aj7YXAWAzaLo1yeVgswUstLspDtsOGwW7FaF3Wrh/otP6fDrK611h/9JIlFKVQE/JNqODpIHJN8YmfbTHc7jgNb6vES8cBf7ajJfe7Gt/eQB33UzX03WaxmJ7mQrJL+9PXVdbS/J/j7FSk8+j5h9tdsHyD0BpdSXWutxibajo/SU8+iOJPO1F9vaT7LadSS6k83dyVbofvb2VnrK+yTnYSBNeoIgCIIgCILQAgmQBUEQBEEQBKEFEiAnB08m2oBOoqecR3ckma+92NZ+ktWuI9GdbO5OtkL3s7e30lPeJzkPpAZZEARBEARBEMKQDLIgCIIgCIIgtEAC5ASgDJ5TSn2qlHpTKZWhlFqplNqolFqqlFKJtjFWlFJ3KKXeU0rlKaX+Vyn1T6XUA4m2qycSwW/a6Jgrpc5TSlUopT4K/oyMk21HfV2lVGoi/FwpdWYLu8qVUlcfi/2dbJNdKfXfwdsxXZdEXb8jkYw2hYhlnU1G+4+0pso6mxiO1ZeS0b+g/T6WjH6nlLo3aNMqpVRBV5yHBMiJYTJg01pPBPoA1wEVWuvRQA5wbiKNixWl1CDgmuCf/wr8HRgNnK+UGpEou3owrf1mSpTHLdJanxH82Rw/8476urNIgJ9rrdeE7AI2ARuiPDQu100p5QTWcfj8Y70uCbl+RyEZbQoRyzqbVPbHsKbKOpsYjtWXksq/4Jh9LKn8Til1AnCy1vpHwCrgTxHs6/B5SICcGPYDjwRve4DfA+8G/34fOCsBNh0LjwC/Dt4+G3hXax0A/ofucw7didZ+E43pSqnPlVIr4pyxONrrnk0C/VwplQYM01pvivKQuFw3rbVLa10CVAQPxXpdEnr9opCMNoWIZZ1NNvuPtqbKOpsYjtWXks2/4Nh8LNn87hwgRyn1IfAjYAhdcB4SICcArfVWrfXnSqmLAQdGNqkuePchoG/CjIsRpdSVwEagLHgol252Dt2NCH7zdoSHbQPmaa3HAwOAH8fJvFheN9E+ci6wOsp9ibpuEPt1SfT1i0Qy2gTEvM4mjf0xrqlJY29vogO+lFTvVwd8LKnOA8gHqrTW/wIUAuPpgvOQADlBKKUuAn4JXAhUAlnBu7LoHiMep2J8i3sJKMUY6djdzqHb0dJvtNb+CA85CLwXvL0TKIiTabG87gES6yMXAiuj3Jeo6waxX5dEX79IJKNNJjGss8lkfyxrajLZ26s4Rl9KtvfrWH0s2c7jEBAqg9uOsWZ3+nlIgJwAlFL9gXuAC7TW9RhZrVA96dnAB4myLVa01lcGazovx/g2/TgwRSllwci+Jf05dDci+E0k7gQuD74Po4Cv42ReLK+bMD8PlkychbHNGYlEXTeI/bok4zqRjDYBMa+zSWN/jGvq6gjHhC6mA76UNP4FHfKxZPO7dcBpwdvDMILlTj8PCZATw9UY27hvK6U+AuzAcUqpTRiZrGjbwMnMo8DPMJqg/q61/j7B9vREwvxGKXW9UuqPrR7zZ+Ba4DPgda11Wet/0kWEvS7gimDbchLn56cB32itm5VSQ5LoukGE6xLFxkRev2gko00hYllnk9n+SGuqrLOJ4Vh9KZn9C2L3saTyO631WuCAUuoLjOD4qgj2dfg8ZFCIIAiCIAiCILRAMsiCIAiCIAiC0AIJkAVBEARBEAShBRIgC4IgCIIgCEILJEAWBEEQBEEQhBbYEm2AcOwopZ4F0rXWlyilXgKagUlANeAGZgJ7MSbnnBa8fbnW+khT2AShU1FK2bXW3kTbIQiC0BMIylbagVSM4RcDgBFAEXB/cFKc0EEkg9z9KQn+Hh38PV9rfTrwNHAbhij4YK31JOAb4JL4myj0ch5RSt3c+qBSyhb8XayUejj+ZgnCYZRSw5RST7f4e5VSKlUp9d8tjg1USu1TSq1p9VOhlBod+T8LQqfzH8AnwAsYQz9+DaQBX2IEzeKrnYBkkLs/PqVULtB6qlo24ALOBNYEjz2K8a1TEOKCUmoicD5wvFLqglZ3WzA0Ka8Ezg8u2vXAWg6L69uAK7XWFXEyWei9+IAmpVQaxk5cdlA3O08pdTbwLW3X2Za44mGk0LtRSp2O4YehqaDjgn8XBH8mKKXe5/CkuUiIr8aABMjdn6+Ay4K/AX6jlLoJKAduAR4EdiqlZgO3AsswhiIIQpeilBoCLMLIdvQFWoqu24AFSqlMjIlGtwInAU8EH/cexljU/9PqeYLQ6QQD4P/E2KKuAWqBPsG7+wCnYpSo7QeWAhtb/YtiwKuUUlqGCwhdyz6M7LEv+Hc24AE+Cv5txfBVL+KrHUIC5O7POuAa4EWMMov7tdbLQncqpeqATK31/1VKVWF82xSEeOADbgK2AemEB7p2rbVWSmVhfGk7FzgOo47+H6EHSS2dEA+01u8rpS4H7tZazwuOoj0rePcO4E/AKRjb2YcwAumWKGAxcBfxHVMu9DK01tuVUvsxkmK7gYFAABiPUYt8RvDY44ivdgipQe7+rMdowFsf5f6PgZ8Fi/ql7kiIG1rrcoya92eAxzB2LkI/fws+rB7Dd3dgZD3+CTTE3VhBMBJGo4L18muAicHjEzB2NPZglAM1YPjvJgy//RtGxvk2rbUEHEI8CGD463sYpT/fBG9bAK213oj4aoeRDHL3ZyewBfghyv1vAucBnwN1HK5HFoQuR2t9b6TjSqn/Cd5fp5T6LfAQRn38b7TWFxnf5wQhPiilpgI3Y5RQ/EVrvUgpFarx/ExrPTX4uAKM9XQwhnpACka98jBAGk2FeGEFcoCfcDiDnAvkc7jPyIL4aoeQALkbo7W+JnhzZPD3NREeozEWfkFIJhwtbrtb3JbmESHuaK1XKqW+Bu4GrlVKzeDwjtt4pdR7GNvS72DshmwDxmAEHu8Bg5CdDyF+5AI3YtQZ/zT4+32MwLkfxm5cM+KrHUICZEEQEkGdUupqjMW7P3Bd8PhwpdRtGJmRfkqpEcC/aa13JsZMobehtV4CLGmRQf68RQb5CgzlFS+Hs3LDMLJ224FX42+x0JtQSlkx6ogjacv7AR2U0LwA8dUOIQGyIAiJYCHGAj1ea31G6KBSalSwNu6xhFkm9DqUUg4Mf1za4nDo8zEl+JixGFm7mhbHvRzOxs1VSu3XWn/Y9RYLvZhJGMpArSlpcft9DIlX8dUOoETlQxCERCAyQ0IyoZSyaa19R3+kIAi9AQmQBUEQBEEQBKEFIvMmCIIgCIIgCC2QAFkQBEEQBEEQWiABsiAIgiAIgiC0QAJkQRAEQRAEQWiBBMiCIAiCIAiC0IL/B5kIGYymHqirAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "sns.pairplot(train_dataset[[\"MPG\", \"气缸\", \"排量\", \"重量\"]], diag_kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>气缸</th>\n",
       "      <td>314.0</td>\n",
       "      <td>5.477707</td>\n",
       "      <td>1.699788</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>排量</th>\n",
       "      <td>314.0</td>\n",
       "      <td>195.318471</td>\n",
       "      <td>104.331589</td>\n",
       "      <td>68.0</td>\n",
       "      <td>105.50</td>\n",
       "      <td>151.0</td>\n",
       "      <td>265.75</td>\n",
       "      <td>455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>马力</th>\n",
       "      <td>314.0</td>\n",
       "      <td>104.869427</td>\n",
       "      <td>38.096214</td>\n",
       "      <td>46.0</td>\n",
       "      <td>76.25</td>\n",
       "      <td>94.5</td>\n",
       "      <td>128.00</td>\n",
       "      <td>225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重量</th>\n",
       "      <td>314.0</td>\n",
       "      <td>2990.251592</td>\n",
       "      <td>843.898596</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>2256.50</td>\n",
       "      <td>2822.5</td>\n",
       "      <td>3608.00</td>\n",
       "      <td>5140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>加速度</th>\n",
       "      <td>314.0</td>\n",
       "      <td>15.559236</td>\n",
       "      <td>2.789230</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.80</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.20</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年份</th>\n",
       "      <td>314.0</td>\n",
       "      <td>75.898089</td>\n",
       "      <td>3.675642</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>美国</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.624204</td>\n",
       "      <td>0.485101</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欧洲</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.383413</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日本</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.398712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count         mean         std     min      25%     50%      75%     max\n",
       "气缸   314.0     5.477707    1.699788     3.0     4.00     4.0     8.00     8.0\n",
       "排量   314.0   195.318471  104.331589    68.0   105.50   151.0   265.75   455.0\n",
       "马力   314.0   104.869427   38.096214    46.0    76.25    94.5   128.00   225.0\n",
       "重量   314.0  2990.251592  843.898596  1649.0  2256.50  2822.5  3608.00  5140.0\n",
       "加速度  314.0    15.559236    2.789230     8.0    13.80    15.5    17.20    24.8\n",
       "年份   314.0    75.898089    3.675642    70.0    73.00    76.0    79.00    82.0\n",
       "美国   314.0     0.624204    0.485101     0.0     0.00     1.0     1.00     1.0\n",
       "欧洲   314.0     0.178344    0.383413     0.0     0.00     0.0     0.00     1.0\n",
       "日本   314.0     0.197452    0.398712     0.0     0.00     0.0     0.00     1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train_dataset.describe()\n",
    "train_stats.pop(\"MPG\")\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分离 label\n",
    "train_labels = train_dataset.pop('MPG')\n",
    "test_labels = test_dataset.pop('MPG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "  return (x - train_stats['mean']) / train_stats['std']\n",
    "normed_train_data = norm(train_dataset)\n",
    "normed_test_data = norm(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 64)                640       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 2,753\n",
      "Trainable params: 2,753\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def build_model():\n",
    "  input_dim = len(train_dataset.keys())\n",
    "  model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=[input_dim,]),\n",
    "    layers.Dense(32, activation='relu'),\n",
    "    layers.Dense(1)\n",
    "  ])\n",
    "\n",
    "  model.compile(loss='mse', metrics=['mae', 'mse'],\n",
    "                optimizer=tf.keras.optimizers.RMSprop(0.001))\n",
    "  return model\n",
    "\n",
    "model = build_model()\n",
    "# 打印模型的描述信息，每一层的大小、参数个数等\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 1000/1000"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "    \n",
    "EPOCHS = 1000\n",
    "\n",
    "class ProgressBar(keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs):\n",
    "    # 显示进度条\n",
    "    self.draw_progress_bar(epoch + 1, EPOCHS)\n",
    "\n",
    "  def draw_progress_bar(self, cur, total, bar_len=50):\n",
    "    cur_len = int(cur / total * bar_len)\n",
    "    sys.stdout.write(\"\\r\")\n",
    "    sys.stdout.write(\"[{:<{}}] {}/{}\".format(\"=\" * cur_len, bar_len, cur, total))\n",
    "    sys.stdout.flush()\n",
    "\n",
    "history = model.fit(\n",
    "  normed_train_data, train_labels,\n",
    "  epochs=EPOCHS, validation_split=0.2, verbose=0,\n",
    "  callbacks=[ProgressBar()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>mae</th>\n",
       "      <th>mse</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_mae</th>\n",
       "      <th>val_mse</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>3.409430</td>\n",
       "      <td>1.230940</td>\n",
       "      <td>3.409431</td>\n",
       "      <td>8.946074</td>\n",
       "      <td>2.340401</td>\n",
       "      <td>8.946074</td>\n",
       "      <td>997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3.577053</td>\n",
       "      <td>1.250938</td>\n",
       "      <td>3.577053</td>\n",
       "      <td>9.061930</td>\n",
       "      <td>2.383534</td>\n",
       "      <td>9.061931</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>3.459321</td>\n",
       "      <td>1.242559</td>\n",
       "      <td>3.459321</td>\n",
       "      <td>9.225462</td>\n",
       "      <td>2.375621</td>\n",
       "      <td>9.225462</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loss       mae       mse  val_loss   val_mae   val_mse  epoch\n",
       "997  3.409430  1.230940  3.409431  8.946074  2.340401  8.946074    997\n",
       "998  3.577053  1.250938  3.577053  9.061930  2.383534  9.061931    998\n",
       "999  3.459321  1.242559  3.459321  9.225462  2.375621  9.225462    999"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = pd.DataFrame(history.history)\n",
    "hist['epoch'] = history.epoch\n",
    "hist.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "  hist = pd.DataFrame(history.history)\n",
    "  hist['epoch'] = history.epoch\n",
    "  plt.figure()\n",
    "  plt.xlabel('epoch')\n",
    "  plt.ylabel('metric - MSE')\n",
    "  plt.plot(hist['epoch'], hist['mse'], label='训练集')\n",
    "  plt.plot(hist['epoch'], hist['val_mse'], label = '验证集')\n",
    "  plt.ylim([0, 20])\n",
    "  plt.legend()\n",
    "  \n",
    "  plt.figure()\n",
    "  plt.xlabel('epoch')\n",
    "  plt.ylabel('metric - MAE')\n",
    "  plt.plot(hist['epoch'], hist['mae'], label='训练集')\n",
    "  plt.plot(hist['epoch'], hist['val_mae'], label = '验证集')\n",
    "  plt.ylim([0, 5])\n",
    "  plt.legend()\n",
    "  \n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[===                                               ] 73/1000"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
    "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
    "                    validation_split = 0.2, verbose=0, \n",
    "                    callbacks=[early_stop, ProgressBar()])\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集平均绝对误差(MAE):  1.82 MPG\n"
     ]
    }
   ],
   "source": [
    "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\n",
    "print(\"测试集平均绝对误差(MAE): {:5.2f} MPG\".format(mae))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x139423c50>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pred = model.predict(normed_test_data).flatten()\n",
    "\n",
    "plt.scatter(test_labels, test_pred)\n",
    "plt.xlabel('真实值')\n",
    "plt.ylabel('预测值')\n",
    "plt.axis('equal')\n",
    "plt.axis('square')\n",
    "plt.xlim([0,plt.xlim()[1]])\n",
    "plt.ylim([0,plt.ylim()[1]])\n",
    "plt.plot([-100, 100], [-100, 100])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
